DE3943563C2 - Polygonal information encoding article, process and system - Google Patents

Abstract

The method of decoding an electrooptically readable identification carrying a code in the form of a number of polygons, with their centres on points of a two-dimensional grid and each with two or more optical characteristics, involves locating the polygons and determining their optical characteristics. For locating the polygons the region of the identification in which the information is encoded is transformed into the frequency domain.The periodicity of the two-dimensional grid is determined in the frequency domain to determine the geometrical locations of the polygons.The geometrical locations are associated with the optical characteristics of the polygons.

Description

The invention relates to a method for decoding an optically readable identification according to the preamble of claim 1 and an apparatus for carrying out the method. Such markings are placed on a substrate brings in order within a given two-dimensional Data field to record information and store where where the data field consists of a number of polygons, each one of at least two different optical Have properties.

Goods, individual parts, letters, packages, containers and others Objects to be sent or transported, are repeated regarding the origin, the flight number, the Destination, name, price, part number and identify numerous other pieces of information. A another application involves reading coded informa on labels or markings to automatically get sales and inventory grasp or fed to electronic cash registers to be able to. Other uses of such coded Labels include automatic sorting and Forwarding of mail, parcels, luggage and the like, and the affixing of markings with manufacturing instructions on raw materials or individual parts for a manufacturer process. The markings for such articles are conventionally provided with a bar code, for example the UPC barcode. It is quite a number of different known to bar code systems.

However, the conventional bar code systems assign one low data density to meet the requirements for storage of more and more information on ever smaller labels or to meet markings. A reduction in Size and spacing of the strips in different Bar code systems do not provide a solution to the problem; Systems and optical scanners with sufficient resolution for the festival Positioning of strips that are 75 µm or less apart, and the required small tolerances during the printing process are just not economical. To get a bigger data menu ge, very large labels must therefore be used with the result that these labels are no longer on smaller items fit. Another essential fac tor is the cost of the label material, for example wise paper. The paper for a smaller label costs less than that for a large label; for large quantities these costs are a significant factor.

Well-known alternatives to barcodes are: Round formats with radially arranged, wedge-shaped code elements (US-PS 3 553 438) or concentric black and white bit coding te rings (U.S. Patent 3,971,917 and U.S. Patent 3,916,160); Grid off Rows and columns of coded squares or rectangles (U.S. Patent 4,286,146); microscopic points that appear in cells are ordered, which form a regular grid (US-PS 4,634,850); and densely packed multicolored data fields Points or elements (U.S. Patent 4,488,679). Some of these bei games do not have sufficient data density; about the ko dated round pattern or the grid of rectangular or square faces. The grids with microscopic points or with multicolored elements require a special one Alignment and promotion in order to be read, whereby their usability is limited accordingly.

Due to the size and speed of modern transport systems with, for example, conveyor belts from 90 to 120 cm wide and belt speeds of 2.5 m / sec or more that transport objects of different heights, and the need for small, cheap, compact labels or tags with a An area of approximately 6.25 cm ² (one square inch) has gone to great lengths to detect and read the encoded indicia on these fast moving objects. The captured or identified image of the marking must be correctly decoded before the object on the conveyor belt reaches the next working position, often in fractions of a second. There is therefore a need for a simple, fast and inexpensive means of signaling the presence of an encoded identifier in the field of view of an optical scanner which is mounted so that it can scan the entire conveyor belt. At the same time, the data should be arranged with high density.

Data fields with acquisition targets are already known for Example of concentric geometric figures with rings, Squares, triangles, hexagons and variations of how in U.S. Patents 3,513,320 and 3,603,728. In the U.S. Patents 3,693,154 and 3,801,775 is the application of Sym bolen described as identification and positional indicators contain concentric circles, with the sym bole attached to the objects to be optically scanned are. However, these known systems include the Ver use of two separate symbols to determine the ids identification of the data field and its position, whereby the logical scarf required to identify the symbols processing becomes complicated and which also changes the data capacity of the associated data field is reduced. At this Using two symbols will damage one sym bols to difficulty in locating the position of the data field and the associated ability to Obtaining information from the data field. Both latter systems are on opposite ends of data tracks with encoded linear identifications separate position and orientation marks are used, with the consequence that the ability to record data is limited.

The above systems use an optical sensor sampled, which generates an image output signal that represents the Changes in the intensity of the light emitted by the Data field and the position and orientation symbols is reflected. The output of such systems has a certain bitmu after its digitization ster, which is compared with specified bit sequences. These However, systems have the disadvantage that two separate Sym bole are required to capture the figure first and then determine its orientation. Also can with Process of comparing the digitized output signal les of the optical sensor with predetermined bit sequences that represent both the position and the orientation symbols set, readout errors occur easily, as with the known characterization systems the detection target signal level is not flexible.

In U.S. Patent No. 3,553,438 there is a circular data array described with a centrally arranged acquisition target, which has a series of concentric circles. The he objective provides a device for capturing the circle shaped label by the optical sensor and the Be mood of its geometric center and thus the geometric center of the circular data arrangement This determination is made by a logic circuit that recognizes the pulse pattern that is the circular shape of the detection target. As with the bar codes, however, the Data field only a small data capacity, and the system requires a second circular scan. Using a linear and circular scanning with such a Sy system with limited data capacity would be very costly to achieve only a slight increase in the data capacity compared to traditional bar codes be things; matters.

To increase the data capacity, codes with tightly grabbed multicolored dots designed as they are in the U.S. Patent 4,488,679 from which the preamble of claim 1 starts. To read the multicolored dots must be marked in a precise manner specific alignment to a CCD sensor of a scanner can be mapped. This makes it completely impossible to yourself fast moving data fields on an object on a record and decode fast conveyor belt. High dense code systems with microscopic data points, as in in U.S. Patent 4,634,850 require special trans port facilities to ensure that the data field moves exactly in a given direction, and are in any orientation, as it is when transporting Ge objects on conveyor belts are not applicable. The coded label must be read track by track, for which a linear scanner is provided, which is connected to the Trans port facility is connected. The position of the label be with regard to the sensor must be adhered to very precisely, to read the label correctly.

In connection with bar codes, multi-color Systems used to solve the optical problems of scanning to overcome closely adjacent strips. A bar code with more than two optical properties for coding Da ten in a data field, for example by alternating en use of black, gray and white stripes, is in U.S. Patent 4,443,694. However, you can also use such systems did not increase the data density that much that it meets the requirements.

The object of the invention is therefore to provide a method and a device which is able to always a marking from changing distances and directions safe to read. This should also succeed if the labeling has a high information density.

The solution to this problem is achieved with the one in claims 1 and 20 specified invention.

With the specified method and device, the Labels read by an optical sensor even if they are on an object, which is moved at high speed, with the Orientation and the height of the object are irrelevant.

The optically readable marking can can be reliably decoded, even if the label is tilted, is curved or distorted.

Preferred embodiments are specified in the subclaims.

A method and a device for determining the position of the Marking that passes an optical sensor with high Speed, is in claim 17 or Claim 30 stated.

The polygons of a marking that can be used together with the invention can be regular or irregular be regular, and the grid can be in the plane of the characteristic drawing two or more axes with the same or different axes Have angular distances.

The polygons on the marking can be complete, partial wisely or not at all related. The latter both arrangements result in a number of spaces between neighboring polygons. These spaces can the same or different optical properties as the buttocks have lygons. It can be composed of two-dimensional arrangements hanging polygons with five or more sides for the Marking can be used. Also can be two-dimensional Arrangements regular or irregular and partial or discontinuous polygons with three or more Pages are used when they are on specific axes such a field can be arranged.

In addition, the identification can be an Er target of a number of concentric rings that makes it easier to locate the marking, especially with dynamic reading systems.

The data field a label that can be used together with the invention a generally 6.25 cm square array Area with connected hexagons, the rows and Form columns, and with an Er in the middle target with a geometrical center that represents the geometric center of the data field determined, include. The he target can be any geometric shape with opti have properties that make an easily recognizable image signal result when it's along with an optical sensor a linear scan line is scanned by the The center of the acquisition target. The capture target preferably consists of a number of concentric c's rings of alternating reflectivity, which have a periodic cal image signal when scanned in a straight line become. By using analog filters in the acquisition and decoding of the data field is done by the optical Sen sor generated signal directly at a given frequency compared, enabling a quick and accurate check of the Frequencies and then determining the location of the data field on the substrate is possible. That from the optical sensor output electrical analogue signal that corresponds to the encoded In formation of the marking is then digitized and decoded. The use of a filter step with an analog bandpass filter allows the identification to be recorded without the need for the encoded information to decode the marking. By grasping the means point of the acquisition target can be a reference point on the Data field can be determined. When the center of capture the target is in the center of the marking, can at the same time determine the center of the He target and the data field.

The optically readable data array of the identification can contain up to several hundred error-proof alphanumeric characters on an area of 6.25 cm ² if hexagons with three reflective properties, for example the colors black, white and gray, are used. For a sensor with a given optical resolution, the system according to the invention allows the information to be packaged much more closely than would be possible with a bar code system. For example, when a high resolution optical sensor is used with the system of the present invention, hundreds of alphanumeric characters can be accommodated in an area of 6.25 cm². On the other hand, a hundred characters can easily be detected on the same area with a relatively poorly resolving sensor.

For markings that are preferably used together with the Invention are used, the data is encoded by encoding a number of bits from a binary bit sequence into a Group of adjacent hexagons, each being hexagon has one of at least two optical properties, although the coding is alternatively also hexagon for hexagon could be executed. The digital bit sequence can be stored on the Based on data entered manually by a computer are generated or otherwise converted into the binary bit sequence can be converted, and it can also be a previously recorded nete digital bit sequence can be used. The ones to be coded Data are given certain geometri in a certain sequence between areas of the data field assigned to the number the transitions between hexagons with different opti to increase its properties.

The information to be encoded is preferably stored in In high and low priority formations split the separated in different geometric areas of the data can be drawn in the field. Optionally, the Informa high priority options in the areas for the information lower priority are duplicated to the likelihood possibility of losing high priority information rity due to scanning errors due to dirt, cracks, fal and other damage in the data field. The In high priority formations should be in a central area of the data field, close to the preferably intended capture objective, be enrolled to get the information before employment to protect endings that tend to be in the peripheral areas of the Data field occur. In the data field you can use the large information capacity Possibilities for error correction are provided will see.

The polygons in the data field, preferably hexagons, The data contained therein are preferred as follows read: The coded markings go through a forward given illuminated area and are thereby through a electronically operated optical sensor or one in the Hand held scanner being scanned. The optical scanner er generates an output signal that is an electrical analog signal is that of the intensity of each reflective property properties of an area of the marking corresponds to how it is recorded by the pixels of the optical sensor becomes. The analog signal of the op table sensor first with a predetermined frequency value compared to that of a specific acquisition target speaks if this is provided in the data field. If a match has been found, the identifier is used drawing and the center of the acquisition target determined, which also provides a reference point for the data field is determined. At the same time the analog signal is conti Naturally digitized in an analog / digital converter and stored in an image memory. The saved digital data representing the entire marking, are then available for further processing and decoding Available.

Through logical circuits with stored programs who the digital data in a mapping of the interfaces of polygons with different optical properties puts. This is preferably done by calculating the stan standard deviation of the intensities caused by the optical Sensor at each pixel and a predetermined group of Pixels were recorded around this. Great standard deviations correspond to transition areas at the cut represent contrasting polygons.

The digital data are then processed further, under among other things through filter programs to determine the orientation tion, the direction and the spacing of the polygons. The we The main procedural steps are:

• 1. Filtering the non-linear, transformed version of the digitized illustration;
• 2. Determination of the orientation of the marking, preferred wise by capturing the three axes of the figure and Determination of which axis is the characteristic on two sides drawing is parallel;
• 3. Find the center of each hexagon and Be agreeing the gray tone at the center;
• 4. converting the gray tones into a bit sequence;
• 5. Optionally, perform error correction; and
• 6. Optionally, converting the bit sequence into a specific one Character set.

Embodiments of the invention, as well as usable together with the invention optically readable labels, their attachment to an object and their decoding are explained in more detail below with reference to the drawing. It demonstrate:

Fig. 1 is a detection target with concentric rings;

Fig. 2 is a section of an optically readable identification with adjacent hexagons;

Figure 3 is a complete first embodiment of an operating table readable marking with nebeneinanderlie constricting hexagons with three different optical characteristics and with a detection target.

Fig. 4 shows a group of 3 × 3 hexagons, which serves as a basic Ko dieinheit;

Figure 5 is a graphical representation of the groups for a data field with 33 rows and 30 columns, forming a grid of 11 rows and 10 columns.

Figure 6 shows a device for adjusting the position of an optical sensor to the height of erfas to send objects.

Fig. 7 is a flow chart for the decoding process;

Fig. 8 is a flow chart for recording the detection target;

Fig. 9 is a flow chart for the coding and decoding program and the flow of data therein;

Fig. 10 is a flow chart showing the steps involved in processing the image;

11 is a group of contiguous regular hexagons whose centers lie on a hexagonal grid PERIODIC gen.

FIG. 12 is a group of contiguous irregular hexagons whose centers lie on a hexagonal grid lar irregular shapes;

FIG. 13 is a group of partially coherent poly Gonen of substantially the shape of hexagons whose centers lie on a hexagonal lattice;

FIG. 14 is a group of contiguous polygons are of substantially the shape of hexagons whose centers are on a hexagonal lattice;

Fig. 15 is a designation with contiguous polygons substantially in the shape of hexagons with centers on a hexagonal grid and with a detection target;

FIG. 16 is a group of non-contiguous squares whose centers lie on a hexagonal lattice;

FIG. 17 is a group of non-contiguous rectangles with gaps, whose centers lie on a hexa gonal mesh;

FIG. 18 is a group whose centers lie of noncontiguous pentagons on a hexagonal lattice;

Figure 19 is a group of contiguous rectangles which adjoin each other and whose centers lie on a hexagonal grid. and

Corners Fig. 20, a group of partially contiguous eight, whose centers lie on a rectangular grid.

On the marking are by means of the contrasting color beneath adjacent hexagons or cells contain information th, which are detected by an electro-optical sensor can.

Polygon cells other than hexagons can also be used if the geometric centers of adjacent polygons lie on the corners of a hexagonal or other pre-given grid. In FIGS. 16 to 18, the coding units of such polygon cells are represented by a closed dashed line. The cells are always arranged in a certain pattern. A wide variety of polygons can be used, and grids of different geometry, such as hexagonal, rectangular or square grids, can be used. "Adjacent" polygon cells can be connected, partially connected, or not connected at all.

"Connected" are polygons whose boundaries are different touch along the entire periphery so that none Result in gaps. "Partially connected" polygons are arranged so that the boundaries are along the Touch the periphery in places and not touch in places stir, creating a number of spaces between the Polygons are created. "Not connected" are polygons, which along their limits have no contact at all with the surrounding polygons.

The polygon cells and the grids on which the funds are located points of the polygons can be irregular, with unequally spaced axes, or regularly, with the same spaced axes. The grid axes can be independent of the axes of symmetry, if any, of the polygon cells.

For coding information, hexagons have a few major benefits, especially the following:

1. For a given optical resolution, there can be hexagons packed more densely than other polygons. For example are the corners of squares for a given resolution difficult to see, so to "read" squares a optical resolution, which is unnecessary in itself, is required. Circles the space in between would be ideal for optical resolution however, between adjacent circles is wasted and complicates the production and printing of the label, because the spaces in between have their own optical properties is to arrange.

2. A grid of neighboring hexagons has three axes. In the case of a square or rectangular mark The main axis of the hexagons can be shaped by a predetermined bene relationship to one side of the marking slightly lo be calibrated. This localization of the main axis of a The hexagonal grid makes it easier to read out the coded Data.

A separate unit is to be used here under "Identification" are available by means of a suitable adhesive back a package or product, the outside of a container or can be attached to another object and on which optically readable information is printed.

With "optically readable data field" or "data field" for short is a pattern of adjacent hexagons or cells with two or more optical properties meant that too serves to retrieve a data record by means of the respective optical properties and the mutual to represent the spatial relationship of the cells.

The pattern of adjacent hexagons with a maxi paint number of transitions from hexagon to hexagon to optimal readout and for a maximum storage density referred to as "honeycomb pattern".

The contrasting reflective properties of each Hexagons or cells of the data area can be linked to the ver various kinds can be produced. The term "print" has the general meaning of applying Ma material with certain optical properties on one Substrate or changing optical properties, such as it for example in the so-called "thermal" printing of the Case is. The term "printing" also includes omission the application of a material with a certain opti property on an area of the substrate that itself has a different optical property. To the Formation of black and white hexagonal cells is For example, it just required the black cells too print when the substrate is white.

Under "optical properties" are the light absorption, the reflection and / or refractive properties of the cells understand. If cells are black (dense black print color be) and gray (halftones of black) are printed as well as white are (no print on a white substrate), the Kenn drawing three different optical properties.

As shown in Fig. 1, a "number of concentric rings" 10 are to be understood as two or more concentric rings 12, one of which is formed by a circular area 15 , which is defined by the smallest radius "r" of the rings is.

Fig. 2 shows a section of an electro-optically scannable marking according to the present Ausfüh approximate form. The label contains a number of neighboring hexagonal cells which are arranged in a honeycomb pattern. Each of the individual hexagons is denoted by the reference number 20 and has six identical sides 22 . The interior angles "a" of the hexagons are also the same size and are each 120 degrees. The hexagons have a long vertical axis yy and a shorter horizontal axis xx.

A label 30 ( FIG. 3), approximately 2.5 cm by 2.5 cm (one inch by one inch), contains approximately 888 hexagons or cells 20 , taking into account that there is an acquisition target 35 around the center of the label a number of concentric rings is provided. The adjacent hexagons 20 form horizontal rows "R" which are defined by imaginary lines 31 , and vertical columns "C" which are fixed by imaginary lines 33 . In this example the label has a total of 33 horizontal rows "R" and 30 vertical columns "C". Each individual hexagon has a "diameter" of approximately 0.8 mm. Due to the geometric packing of the hexagons, there are more rows "R" than columns "C" in a square border of a honeycomb pattern.

As can be seen from Fig. 2, the hexagons are arranged in ver offset and overlapping vertical columns with alternately vertically spaced hexagons with collinear yy-axes. The yy axes of spaced hexagons 20 are aligned with the outer vertical sides 22 of the intervening, displaced hexagons. The yy-axes of the hexagons 20 in the illustration of FIG. 3 run parallel to the two vertical boundaries 32 and 34 of the marking. The horizontal lines "R" are measured through the xx axes at the center of the hexagons 20.

The hexagons 20 are ausgebil det by a printing process in which the hexagons 20 are printed in two or more different optical properties, such as contrasting colors. These colors can be the colors white and black, so that white hexagons or cells 25 and black cells 26 are produced, as is shown in FIG. 2, and gray can also be used as a further color, so that gray cells 27 are produced as shown in FIG. The color tones white, black and gray were chosen in the present embodiment because they produce an optimal contrast that facilitates identification by an electro-optical sensor. The shade of gray is chosen so that its optical properties are roughly in the middle between the optical properties of the white and black color.

The identifier of Fig. 3 may represent a separate label that has an area of 6.25 cm ² (one square inch). Alternatively, if there is an acceptable color background (preferably white), the identification can also be printed directly onto the surface of an object. Due to the importance of a defined background for the optical properties of the contrasting colors, however, it is often more advantageous to use a separate label, since its background color is easier to control.

The alignment of the hexagons printed on the label with respect to the sides of the label is important for the later determination of the main axis of the label. The label is printed so that the yy axes of the hexagons in the illustration of FIG. 3 are parallel to the vertical sides 32 and 34 of the label.

For "reading" the hexagonal arrangement it is for decoration dation of the information is important, a sharp color contrast to have between adjacent hexagons. The scanning front direction and the software required for decoding the easier it is, the fewer the optical properties Coding of the hexagons can be used. Less optical However, properties also reduce the data density of the Labelling. As a compromise between the amount of encoded Information that is stored on the label can, and the effort for scanning multicolored ident Drawings has become the use of three different ones optical properties, especially the colors black, Gray and white, turned out to be beneficial.

The gray hexagonal cells are made by printing the Black ink cells in every fifth pixel of the raster of a dot matrix printer, namely using a halftone algorithm like it all is commonly known. The printer thus prints a certain th proportion of pixels to define a given gray hexagon, while when printing a black six every pixel of this hexagon is printed out. The halftone algorithm used has the program name "LABEL".

The black hexagonal cells can be printed with an ordinary black ink. The Scan analysis software for the decoding of the ident tion clearly distinguishes between the reflectivity of Black, gray, and white, so no particularly accurate feast laying of the color is required. However, if other colors be used, or when different shades of Gray used to represent four- or five-color data fields to produce, the contrast of the printing inks must be more accurate trolled to detect measurable differences in the optical To ensure characteristics of different colors. The However, using a black color is the easiest and easiest way of producing a honeycomb-shaped arrangement tion of hexagonal cells with three different opti properties.

Because of the square shape of the label and the nature of the hexagonal cells, there are incomplete hexagons 56 at the edges of the honeycomb structure; As can be seen from Figure 3, these incomplete hexagons are not used to include any useful information.

The present embodiment of the label also includes a detection target. The detection target 35 shown in Fig. 3 consists of a number of concentric rings of contrasting colors (shown as black and white). The black rings are designated with the reference characters 42 , 46 and 48 and the white rings with the reference characters 44 , 50 and 52 . The detection target is preferably located in the geometric center of the marking in order to reduce the likelihood of damage or destruction if the edge of the marking is scratched, soiled or damaged. Also, the size of the image memory that is required to store the data before the identification of the acquisition target is smallest when the acquisition target is in the center of the identification.

The number of concentric rings for the acquisition target is arbitrary, but it has been found that six kon centric rings are sufficient.

To adapt a calculated pattern to what is expected becomes what the concentric rings represent if that Pattern is read, a pattern correlation process is used used. In the event of a match, the acquisition goal is localized, as explained in more detail below becomes. The generated in the present embodiment and used special filters has the file name "FIND.C".

The acquisition target has an overall diameter that is smaller than the data field and comprises an area of up to 25%, preferably about 7%, of the area of the data field. The acquisition target should be as small as possible, since the area occupied by it cannot contain any information on the label. In the present embodiment, the outer ring 52 has an outside diameter of about 7.45 mm. In the identification of FIG. 3, the detection target 35 thus occupies approximately 7% of the area of the identification 30 of 6.25 cm². This does not unduly limit the amount of information that can be contained in the data field surrounding the acquisition target. As with the incomplete hexagons on the outer edge of the marking 30 , the incomplete hexagons 55 around the boundary of the detection target 35 are not used for the purpose of coding information. The width of each ring is preferably approximately equal to the width of the hexagons (length of the xx axis in FIG. 1) in order to facilitate the resolution. Six rings are sufficient so that the detection target can be localized with the smallest possible area and a minimum of possible false readings due to "disturbing" characters on the license plate or other "disturbing" characters outside the license plate, such as on a conveyor belt.

The acquisition target can also be a form other than have concentric rings. For example, squares, Spirals or hexagons are used to make transitions between contrasting concentric figures gen, as long as straight cuts through the acquisition target regu lear, predetermined and identifiable color transitions result ben that are suitable by an electro-optical sensor detected and measured by means of a suitable filter become. It should be noted that a spiral does not open succession of concentric rings represents that ever but depending on the size and radius of the spiral le is a good approximation of the shape of concentric rings can be reached. A concentric acquisition target Wrestling is beneficial because of the signal that is generated during a scan is generated by the center point, has a frequency that is always the same, regardless of the direction of the cut. This makes it easier to identify the center point, and it allows the location of the capture to be identified target with a one-dimensional search of the analog or digital output signals from the scanner. However, it can alternatively or subsequently also a two-dimensional one Searches are carried out for the purpose of increased accuracy, when analyzing a digital signal.

Under "concentric rings" are complete rings, part rings in the form of semicircles, sectors more concentric Rings that are between 180 degrees and 360 degrees, and Spira len that form approximately concentric rings.

Since each hexagon in the present embodiment can accordingly be encoded according to three different optical properties, 1.585 "bits" (log ₂ 3) of information can be contained in each hexagon. If fewer or more optical properties than three are used, the number of bits that can be accommodated in a hexagon changes accordingly. The coding algorithm is designed to approximate the maximum data density and to make the number of transitions in the optical properties from cell to cell as large as possible in order to facilitate the two-dimensional clock recovery process which will be described below .

Fig. 4 shows a group of 3 × 3 cells, i.e. nine hexagonal cells 60 , the basic coding unit used in the present embodiment. This is one of many possible, essentially equivalent, coding forms. The group of 3 × 3 cells of hexagons 60 is intended to represent 13 bits of information coded if the group contains nine complete hexagons, or to represent fewer than 13 bits if the group is incomplete by unusable hexagons. A label with an area of 6.25 cm² and a data field with around 888 hexagons and a detection target of around 7% of the label area can accommodate around 1292 bits for information.

When coding each group, the outer lower hexagons 62 and 64 of each group 60 , as shown in FIG. 4, are determined in their respective optical properties so that they always differ from the hexagon 66 in between. The hexagons 62 and 64 can therefore only ever correspond to one bit per hexagon. In this way, by coding 11 bits in the remaining seven hexagons, it is possible to accommodate 13 bits for information. Since seven hexagons result in more possible combinations than are used (3 ⁷ = 2187 ^{combinations versus 2 11} = 2048 combinations), certain combinations are not used, for example all cells black, all cells gray and all cells white or all im essential black, gray and white combinations. The reason for requiring contrasting colors for hexagons 62 and 64 relative to hexagon 66 is to ensure a transition required for the clock recovery step and optional normalization process, and to facilitate the determination of a horizontal alignment of the data field .

If the codable group is seven or eight hexagons around The seven usable hexagons become 11 bits and the eighth hexagon, if present, with one bit. For all other subgroups, each pair will be of hexagons three bits and each remaining individual One bit assigned to hexagon.

It can be seen that this marking is a particularly effective marking which is easy to read by means of a suitable scanner and the corresponding analysis software and has a very high information density on a relatively cheap, easy-to-print label or the like. As described, the present embodiment uses a pack of hexagons with 33 rows and 30 columns in an area of 6.25 cm ² and with a detection target which occupies about 7% of the total area of the license plate. A group of nine hexagons contains 13 information bits, so that 1.44 bits are recorded per cell. Because of the other restrictions imposed by the coding algorithm, this is less than the theoretical 1.585 bits per cell, since not all 37 patterns are used and since some of the at least optically possible transitions from cell to cell are not used.

It is beneficial to have some level of error protection in the Provide coding of the marking so that the actual the same amount as that shown on the label recoverable information in favor of a high level of data security is reduced.

The above description of a designation with hexago nalen cells is directly on a label with others Polygon cells transferable. When using the optical egg Properties black and white and optionally gray also apply for other polygon cells the specified advantages, for example wise about the data density. As with the markings with Hexagons can mark with other polygon cells can be read out with simpler scanners, if only two optical properties for encoding the information be used.

The processes for encoding the information and the algo rithms for markings with hexagons are directly on Identifications can be transferred with other polygon cells. How the hexagon markings are not complete Polygon cells at the edge of the marking and around the capture target not used to encode information.

A "honeycomb pattern" includes an arrangement of interrelated hexagons 310 ( FIG. 11), the geometric centers of which 311 lie on the corners or intersection points 311A of a hexagonal grid. Regular hexagons, i.e. hexagons with six equal sides and six equal interior angles, form a hexagonal grid, which in turn is regular and has three axes (A1, A2, A3) that are each 60 degrees apart from one another.

If the hexagons 320 ( FIG. 12) of the identification are not regular, but symmetrical, i.e. if they are pulled apart along two parallel sides 321 and 322 , for example, the geometric centers 325 of adjacent hexagons describe a non-uniform hexagonal grid 327 again . Such a non-uniform hexagonal grid still has three axes (A1, A2, A3), which however no longer have an even angular distance from one another.

Although the hexagonal grid of Figure 12 is not uniform, it is nonetheless a two-dimensional geometric grid with axes at predetermined angular intervals. This also specifies the positions and distances between the center points of the hexagons on the axes. The geometry of the hexagonal grid is used for decoding during the decoding process. In particular, the filtering that is carried out on the transformed digital data that corresponds to the image detected by the optical sensor is abge on the predetermined geometry of the marking. The reconstruction of the figure also provides missing grid points. Missing grid points arise from the fact that there are no optical transitions between polygons with the same optical properties.

In the case of non-uniform hexagonal grids of the type shown in FIG. 12, it is advantageous to turn off the main axis determination step 3 e of FIG. 7, which is carried out after a Fourier transformation to identify the main axis of the marking. On the main axis of the identification, the centers of the polygons lying on this axis have a different distance than on the other two axes.

Refinements of the identification are possible which approximate the preferred embodiment with hexagonal cells using specific polygon cells. Fig. 13 shows the structure of a marking with polygonal cells 330 which substantially form hexagons, however, consist of 20-page polygons. The polygons can of course also have more or less than 20 sides. The polygons are only partially adjacent to one another, in contrast to the imaginary hexagonal cells 331 in which they are drawn and which are completely connected.

The spaces 332 of FIG. 13 may or may not have a different property than the encoded polygons. Gaps do not contain any encoded information; their presence therefore results in a lower data density for a given optical resolution and performance. If the spaces also have a different optical property than the adjacent polygons, more optical transitions are detected by the optical sensor, and the clock signal energy in the transformation range of the decoding process is therefore higher.

Since the polygons of FIG. 13 are arranged on a hexagonal grid 335 with three equally spaced axes A1, A2, A3, the geometric centers 333 of the polygon cells 330 lie on the corners of a hexagonal grid 335 . The distances, positions and spatial orientations of the center points of the polygons are specified and can be determined in the transformation area of the decoding process.

In the arrangement of FIG. 13, polygons are used which are almost in the shape of hexagons. Since they approximate hexagons fairly well, a medium resolution optical sensor will "read" them as hexagons.

Fig. 14 shows an arrangement similar to Fig. 13, but the polygons 340 of Fig. 14 are arranged so that they are completely connected. These polygons 340 can be approximated by an imaginary hexagon 341 , but there are no spaces between the polygons. Such a coherent arrangement is favorable to simplify the decoding process, but not absolutely necessary.

The geometric centers 342 of the polygons 340 are again on a hexagonal grid 345 , are essentially the shape of hexagons and appear as such at a medium optical resolution.

Fig. 15 shows an enlarged detail of a marking, such as occurs when it is printed with a dot matrix printer with 200 dots per inch (2.54 cm). The polygons 360 of FIG. 15 illustrate the shape of the geometric figure which is actually printed out instead of a hexagon due to a low pixel density. Printers with a higher density of dots give a better approximation of the hexagon. The polygons 340 and 360 of FIGS. 14 and 15, respectively, therefore correspond to the shapes which result in practice when producing the identification with some printers. However, polygons of such shapes are also equivalent in practice to cells made up of contiguous hexagons.

As in the case of FIG. 3, the identification of Fig. 15 contains an acquisition target 370 from a number of concentric rings shear 371-376. Like the hexagons in FIG. 3, the polygons 360 in FIG. 15 are also arranged in columns "C" and rows "R", the boundaries of which are formed by the imaginary lines 361 and 362 or 362 and 364 . The center points of the polygons 360 in FIG. 15 also lie on a hexagonal grid which is spanned by equally spaced axes A1, A2 and A3.

If another identification geometry with a quadra tables or rectangular grids are used, the two-dimensional clock recovery process described in fol will be adapted accordingly. He does the other geometry of the given grid It is necessary that the filters used in the filtering process be changed. The filters act on the transformed ones digital data that reflect the optical properties of the Polygons correspond to those in the image area from the sensor were read. These relatively minor changes to the The filtering process can easily be carried out by any person skilled in the art become. If the given two-dimensional grid is not has evenly spaced axes or is irregular in structure is moderate, it may be favorable to have the main axis of the characteristic drawing before performing the Fourier transform then the geometric centers along the Axes are not evenly spaced.

Non-contiguous polygons can also be used to generate an optically readable identification. Fig. 16 shows a hexagonal arrangement of squares 420 that are not related and whose geometric centers 422 each lie on the corners of a hexagonal grid that is spanned by the three equally spaced axes A1, A2 and A3. This configuration can be seen to represent a grid of imaginary hexagons 421 , which can be placed over the polygons 420 , with spaces 425 being formed.

An arrangement similar to FIG. 16 can be constructed using rectangles. Fig. 17 shows a number of rectangles 430, the geometric centers of adjacent rectangles on the corners of a grating are hexagona len, which is A2 spanned by the intersecting axes A1 and A3. Recognizing the hexagonal arrangement is again made easier by the imaginary hexagons 431 , which can be placed over the non-contiguous rectangles 430 , with gaps 435 being created between the rectangles 430 . Fig. 18 also shows a non-contiguous indicator of pentagons 440 , the geometric centers 442 of adjacent pentagons 440 lying on the three equally spaced axes A1, A2 and A3. The geometry of the non-contiguous pentagons is recognized more easily again if imaginary hexagons 441 are placed over the pentagons 440 , with gaps 445 being formed.

Other hexagonal grids can also be built in which the axes A1, A2 and A3 are evenly angularly spaced, but do not correspond to the axes of symmetry of the polygons. Only the geometric centers of neighboring polygons lie on the intersecting axes of the arrangement. Such an arrangement is shown in FIG. 19; it comprises a number of connected rectangles 450, the geometric centers of which 451 lie on the axes A1, A2 and A3.

It is also possible to arrange higher-order polygons on a given two-dimensional grid. FIG. 20 shows a number of partially connected octagons 460 which define a number of spaces 461. The centers 462 of adjacent octagons 460 are on the intersecting axes A1 and A2. The spaces 461 can have a different optical property than the octagons 460 . However, this is not absolutely necessary, since it is the position, orientation and intensity of the optical properties in the center of the octagons 460 , which are located at a predetermined position in the hexagonal grid spanned by the axes A1 and A2, that are most important for the decoding process are.

In the preferred embodiment, the label is 6.25 cm ² to accommodate an acceptable amount of data for 100 alphanumeric characters with a high degree of error protection on a relatively small label. The use of four, five or more different optical properties or colors for the hexagons made it possible to accommodate considerably more information in the same space, but with increased demands on the software and the scanning system for the retrieval of the information. For practical purposes, a coding system with three optical properties, the colors black, gray and white, has proven to be very favorable.

The cell groups do not necessarily have to consist of 3 × 3 Cells exist, there can also be any other shape for the group pen can be used. Any group formation can do it be omitted and the coding algorithm directly on a Patterns can be directed from individual hexagons.

The following is the coding process in the present Embodiment explained.

At the beginning of the coding process there is a certain series of Data that are stated in coded form on the label should. It is believed that the designation for the Shipping is provided and the data is in two fields split that as "high priority information" and May be called "low priority information". More than two priority levels can also be provided be.

If the marking is intended for shipping, you can the high priority information includes nine characters, the for example the zip code of the recipient address of a Object, package or letter. Nine characters are therefore provided because up to nine-digit numbers are international ge postcodes are used. When shipping against the postcode provides the most important information It sets out the general destination of the object des and enables scanning and sorting systems to denote the Object on a conveyor belt or the like to the Bring the right truck, plane, etc.

The low priority information can be, for example the name and shipping address, including the post office routing number, the recipient of the item and information included in invoicing.

The reason for generating information high and never The third priority is the protection of the information of higher priority Priority with special error correction options and around the high priority information more in the middle of the Labeling to be able to attach where the probability damage or destruction is less, and to it to enable the high priority information to be included in the Low priority information is repeated so that, even if selectively destroys the high priority information are disturbing, there is a possibility of the information higher Regain priority from those of lower priority. If the information of high priority is placed in the mitt Larger area of marking it is possible to some For the purpose of only decoding this information, like that that only part of the data needs to be processed, resulting in a higher processing speed. This is the case, for example, when a package is on is located on a conveyor belt and only determines the zip code needs to be the further way of the package on a Control number of conveyor belts.

Due to the lower priority, the corresponding Information on the label is not duplicated. Both the high and low priority information However, various error protection codes and cor correction options include to the likelihood an exact recovery of both types of information to lift.

By using error protection characters as part of the encoded information can be done with a suitable program and a suitable computer the system during decoding Recognize and correct rens mistakes. Like error protection codes is well known.

The Be responsible for the production of a label The operator can enter the required data manually Enter the appropriate computer terminal and the computer activated a printer, marking or label with the information contained in the hexagons higher and low priority. The information do not necessarily have to be in such high and low Priority to be divided, and also is a detection target not absolutely necessary, as is the case with the present Embodiment in the middle of the marking with an An number of concentric rings made up of two contrasting ones Colors is provided, the colors being made up of two of the colors exist, which are also provided for the individual hexagons are.

The operator manually entering the data causes the appropriately programmed computer to encode each entered character and use appropriate field designators to generate a binary bit sequence representing the characters of the information and appropriately encoded to give the high and low priority information and determine the relative position thereof. This operation is performed by the program "TEXTIN.C", denoted in FIG. 9 by the reference numeral 110.

Alternatively, the process can also begin with the to encoding information already as a binary bit sequence are available, for example, obtained from a memory or otherwise generated.

After the binary bit sequence has been generated or after an error-protected bit sequence has been produced as described below, the bit sequence must be implemented in the hexagonal honeycomb form according to a specific pattern for coding. Figure 5 is a group representation showing the individual hexagonal cells of 3 × 3 cell groups arranged as a grid or honeycomb of 33 rows and 30 columns. Each row and column is numbered with row numbers from 1 to 33 and column numbers from 1 to 30, respectively. Some of the hexagons on the right edge of the group map and in the middle of the grid are marked with an X. This indicates that these hexagons do not contain any encoded information. This is because the characters X at the edge represent incomplete hexagons, with the result that each of the corresponding lines contains one less hexagon. The characters X in the middle stand for the spaces occupied by the acquisition target or the incomplete hexagons around the acquisition target. All hexagons that are not marked with an X can contain information. In the present embodiment, each of these spaces is occupied by a black (B), white (W) or gray (G) hexagon. Although, as mentioned, a wide variety of group and assignment techniques can be used, groups of nine hexagons with three rows of three hexagons each are provided for defining certain information bits, and 13 information bits can be included in each such group with nine hexagons.

In a data field with 33 rows and 30 columns of adjacent hexagons, a grid of 11 rows and 10 columns of hexagons can be formed, each containing 3 × 3 adjacent hexagons, as shown in FIG. It is apparent, however, that each row of the groups of 3 × 3 cells in the 11 × 10 group grid contains a group of either seven or eight hexagons due to the geometric packing of the hexagons and that the number of these alternates from row to row. This arrangement thus results in six groups with eight hexagons and five groups with seven hexagons. The acquisition target placed in the middle also creates incomplete groups. Thus, FIG. 5 is a graphical representation of the usable groups of hexagons available for encoding information in a data field of 33 rows by 30 columns.

Groups of nine usable hexagons are encoded using the following algorithm (see Fig. 4):
Extraction of 11 bits of information and assignment to the set of seven hexagons labeled a, b, c, d, e, f and h; and use of the hexagons g and i to represent one bit each in such a way that each of these hexagons differs from the hexagon h. This means that 13 information bits can be represented by a complete group of 3 × 3 cells with nine adjacent hexagons.

For groups with seven or eight usable hexagons:
Extraction of 11 information bits and assignment to the seven first usable hexagons; the eighth hexagon, if present, is used to represent a bit.

For all other incomplete cells:
Assign three bits of information to as many pairs of hexagons as possible. Use remaining single hexagons to represent a bit.

Since coding seven hexagons results in more possible combinations than 11 bits (3 ⁷ = 2187 versus 2 ¹¹ = 2048), some combinations of hexagons are not provided. These unintended combinations are chosen so that they are those that give the fewest number of transitions. To do this, look-up tables are established to form the groups as shown in FIG . Every programmer is familiar with setting up and using such look-up tables. The programs for generating the look-up tables "BINHEX.LUT" 132 and "HEXBIN.LUT" 134 shown in FIG. 9 are contained in a program "MK HEX LUT" 130 .

The use of such a bit allocation scheme enables light the accommodation of 1292 information bits in one Data field of 33 rows and 30 columns side by side gender hexagons.

The order in which the high priority information and the low priority information in the group card will be arranged depending on

• a) the amount of high priority information;
• b) the amount of low priority information; and
• c) the optimal placement of the information higher Priority in a protected place

certainly.

Using the group map of Fig. 5 as a model, a stored mapping program "MKMAPS.C" 140 pre-determines on the digital data stored in a memory how the information - both high and low priority information - is supplied in the group map are distributing.

In order to keep the probability of the occurrence of errors as low as possible and to be able to correct errors, error protection and error correction options are provided. For example, with a coding of 1292 information ^{bits in a field with an area of 6.25 cm 2} with 33 rows and 30 columns of hexagons and an acquisition target that occupies about 7% of the area, it is advantageous to use 36 high-priority information bits, to encode a nine-digit postcode and an additional alphanumeric character that can represent a shipping code. In this example, it is advantageous to use 120 check bits for the high priority information, as dictated by the degree of error correction capability desired. 560 bits are provided for the low priority information; therein 40 bits are included for repetition of the high priority information in the low priority information. In addition, 576 check bits are then provided for the information of low priority in order to obtain the necessary security and the possibility to recover the information of low priority. This example illustrates the significantly wasteful use of check bits for the high priority information than for the low priority information. The example given is for explanation purposes only, and the number of information bits for the various priorities of the check bits etc. can be selected to be larger or smaller than specified, depending on the respective requirements.

A "systematic code" has certain information follow and add a separate error to the message sequence test sequence added. A "non-systematic" code just has if a certain message sequence and includes to together with the message sequence, the error checking sequence, so that the message is no longer separated from it, but is replayed is recoverable. It can be both a systematic and a non-systematic code used for error protection become. The following description includes the usage a systematic code.

The Step of "Inserting Error Detection Symbols" can be systematic and / or non-systematic Codiersy include stems.

There are a number of systematic, linear and cyclical Error protection codes known, for example BCH codes, Reed-So lomon codes and hamming codes. In the present execution form, separate Reed-Solomon codes have been added to the Completeness of the high and low priority information to ensure rity. Reed-Solomon codes are very much us effective and are mostly used when multi-bit characters should be checked for errors. Reed-Solomon codes are well known. However, it can also be of any other type be used by error correction codes. Reed-Solomon-Ko des and other coding systems are, for example, in "Theory and Practice of Error Control Codes "by Richard E. Blahut, Addison Wesley 1983, pages 174 and 175.

In the following some details of the Reed-Solomon-Ko are given by way of example. Certain properties of the Reed-Solomon code correspond to the following parameters:
m = number of bits in each symbol;
n = number of symbols in a block = 2 ^m - 1;
k = number of information or message symbols (number of information bits = km);
t = possibility of correction in the number of symbols = (n - k) / 2.

A nine-digit postcode and a single alphanumeric character for further identification purposes require 36 bits in the above example without error protection. A Reed-Solomon code with the following parameters was selected for the high priority information:
m = 6 (6 symbol bits);
n = 2 ⁶ - 1 = 63;
t = 10;
so k = n - 2t = 43.

Since only six 6-bit messages are used to represent a 36-bit message Symbols are required, 37 symbols remain (43-6) as padding symbols used in the encoder and decoder ent and are not indicated on the label need. The total number of highs for the information Priority on the bits required for marking is there here equals (63 - 37) × 6 or 156 bits.

With this error correction scheme it is possible to achieve a maximum of up to to correct 60 (10 × 6) bit errors, which is 38.5% of the used bits. Due to the large number of The padding symbols it contains make it the big mistake-proof positional capacity of this Reed-Solomon coding is extremely un likely that the high priority information is incorrect can be read out.

The low priority information is encoded with a Reed-Solomon code with other parameters, namely with
m = 8 (8 symbol bits);
n = 2 ⁸ - 1 = 255;
t = 36;
k = n - 2t = 183.

Since in the example described on the marking 1292 Bit coded can be specified, is a total of 1136 bits (1292-156 bits for high priority information and check bits) for coding and for the check bits of the information low priority available. The remaining 904 bits (255 × 8 - 1136) are therefore to be provided as padding bits. The results in 560 bits (183 × 8 - 904) for the information low lower priority and 576 check bits.

To recover the high priority information To secure further, these are lower in the information Priority included again. The one on the information Reed-Solomon error protection applied at low priority kode enables the coding of an additional 86 alphanums ric 6-bit characters with maximum error correction ability of about 25.4%.

With the Reed-Solomon error protection coding above, the total number of 1292 information bits available on the tag shown is distributed as follows:
36 bits for high priority information;
120 check bits for this information;
560 bits for the low priority information (including 40 bits for the repetition of the high priority information); and
576 check bits for the low priority information.

The bit sequence of the data including the corresponding check bits to ensure the information are assigned to the individual hexagons in the group map of FIG. 5 as shown. A large number of distribution patterns can be used, taking into account that the main criteria to be determined are:

• 1. Secure arrangement of high priority information in the vicinity of the acquisition target (if one is in the Data field is provided) or the middle of the identifier tion; and
• 2. Creation of a pattern that is easy to read again can be known.

The error used in the present embodiment coding program is "ERRCODE.C".

The coding for the Reed-Solomon codes requires a multiplication of the information code vector with a generator matrix. This matrix multiplication is carried out using Ga lois field arithmetic. Two elements of the field are added by executing an exclusive-or operation between the two elements. A multiplication takes place via a "log" operation in the Galois field. The log and antilog values are obtained using look-up tables derived from the main polynomials, in particular from 1 + x ⁶ for the high priority information and from 1 + x ² + x ³ + x ⁴ + x ⁸ for the low-priority information ger priority. A utility program "GF.C" 126 ( FIG. 9) generates the look-up tables required for Galois field arithmetic. The look-up tables are calculated and stored in file "GF.LUT" 127 for use in encoding and decoding. The generator polynomial g (x) for the Reed-Solomon code is determined by the following equation:
g (x) = (x + a) (x + a ² ). . . (x + a ^2t );
where a is the basic element of the Galois field.

The generator matrix for the Reed-Solomon code is formed by performing a longitudinal division for each of the rows of the generator matrix. The kth row of the generator matrix is formed from the remainder obtained when performing the longitudinal division of x ^nki by g (x).

The calculation of the generator polynomials g (x) and the generator matrices for the information of high and low priority is carried out according to the auxiliary program “MKRSLUT.C” 125 . The look-up tables for the generator matrices are stored in the "RS.LUT" 128 file.

The markings with the hexagons can be produced using standard printers that are widely available and cheap. A printer with a matrix of 300 × 300 dots per square inch (6.25 cm ² ) is sufficient to print three color (black, gray, white) markings with 888 hexagons plus a central acquisition target. Such a printer is, for example, the Hewlett Packard Laser Jet Series II model with a 0.5 megabyte memory and a graphic resolution of 300 dots per inch (48 dots per centimeter). A grid of 300 × 300 pixels with a density of 90,000 pixels per square inch results in about 90 pixels per hexagon. A value 0 or 1 is assigned to each pixel, depending on whether the pixel is black or white. The printer can be used to print a two-color data field of black and white hexagons or a three-color data field of black, white and gray hexagons when a halftone algorithm is used to generate the gray hexagons.

As shown in FIG. 9, a program "MKMAPS.C" 140 is used to generate an area look-up table "REGIONS. LUT" 141 with 34 rows and 30 columns, which is analogous to the illustration in FIG in addition, as it is intended to indicate the selection of black or white for the acquisition target rings. The individual hexagons are each coded in black, white or gray or cannot be used. A separate look-up table "HEX." Is created by a stored subroutine of the program "MKMAPS.C", which defines the association of each of the 300 × 300 pixels in the grid with certain areas in "REGIONS.LUT" 141, ie approximately 90 pixels per hexagon MAP.LUT " 142 is generated. The image points belonging to the detection rings are coded either in black or white. The acquisition target rings are printed by first forming a hexagonal pattern for each area line and then generating the rings. The areas partially or completely covered by the acquisition target rings are indicated in "REGIONS.LUT" 141 as unusable.

The error-protected, coded bit sequence is transmitted according to a predetermined sequence into the field of 11 × 10 groups of hexagons. The sequence is determined by a look-up table "ORDER.LUT" 151 which is generated by a stored auxiliary program "ORDER.C" 150 . The assignment of values 0, 1 or 2 to areas that are available for printing on the identification, while areas with a value of 3 are left unchanged, is carried out by the program "PRLABEL.C" 160 . Shades of gray for the hexagons in the groups of 3 × 3 cells are assigned in conjunction with a program entitled "CELL CODE.C" 170 .

In this auxiliary program it is provided that the information of high priority is preferably stored in an area around the acquisition target in which the probability of damage to the marking is lower. The program "LABEL.C" 180 is used to generate a bit sequence that is suitable for input into the laser printer.

Printing of labels in black, gray and white is an easy process as only black printing color is required when an ordinary halftone algo rithm is used. If other color combinations are specified are seeing what is readily possible are accordingly more complex printers required.

Once each of the printer's image points has been assigned a value for black or white, the identifications can be printed out accordingly to produce a coded area as shown in Fig. 3 in which some hexagons are white, others gray and again others are black and in which a detection target of concentric black and white rings is formed at the center.

The following is the process of decoding or off evaluation of the license plate explained. It is obvious, that the evaluation is preferably carried out at a very high speed on the order of a fraction of a second is done to improve the effectiveness of the handling of the item (or other manipulation) to increase.

There are basically two ways of capturing the image when reading out the identification. The label can be read at a relatively slow speed using a hand-held static fixed focus scanner. Alternatively, an electro-optical sensor with a servo-controlled focusing mechanism can be used for dynamic, fast scanning of fast-moving objects of various sizes and heights. The method described below and the corresponding device for decoding are tert erläu in connection with the use of a fixed focus scanner. However, the process can also be transferred to a dynamic scanning system with certain modifications in the optical system. In order to manipulate objects at high speeds, it is preferred to employ a high-speed scanning mechanism capable of reading indicia as they pass a fixed scanner position at a linear speed of about 2.5 m / sec or more. Fig. 7 shows the sequence of steps of the decoding process, and the mapping processing includes the steps described below.

When an object, package or letter is transported on a fast conveyor belt, the area to be illuminated is relatively large and the dimensions of the objects on the conveyor belt are different. For example, 1 m wide conveyor belts and packages with a width of a few centimeters to almost a meter and corresponding heights are not uncommon for Paketverteilsy systems. The marking with an area of 6.25 cm ² can be anywhere on the entire width of the conveyor belt. The packages can also be inclined with respect to the direction of movement. The packages, letters and the like have different heights, so that the labels to be scanned can be only a few centimeters above the conveyor belt or almost a meter above the conveyor belt, according to the maximum height provided by the respective system.

In order to illuminate the markings sufficiently, is below Consideration of the large range of possible packages widths and heights and at possible display angles Use of an intense light source beneficial to the optical properties chosen for labeling were reflected in a defined way. The light can be in the infrared, ultraviolet or visible wavelength range and at for example in the visible range of the spectrum at any ger place.

The light source for the lighting must therefore be strong enough to produce enough reflected light on the light sensor (z. B. a CCD element) so that the sensor is reliably between the black, gray and white or other optical properties used by the hexagons can divorce. In a dynamic scanning system, an LED array can be used to obtain an illumination of approximately 10 mW / cm ² at the level of the mark. The LEDs can be arranged flat, without a focusing lens, or linearly, with a focusing cylinder lens. A laser light source can also be used in conjunction with an optical system that generates linear illumination.

The choice of light source with certain properties can therefore be made according to the requirements for the respective application. It must be taken into account that the lighting must be sufficient to identify the markings sufficiently quickly, and that the marking itself only has an area of 6.25 cm ² , can be located on a conveyor belt at different heights and with Moves at speeds of up to 2.5 m / sec.

When the above-mentioned static fixed focus scanner is used, an illumination of about 2 mW / cm ² , which can be obtained from a fluorescent lamp, is usually sufficient.

The second step in detection during decoding process is the optical detection of the illuminated Be rich with an electronically operated sensor. The Ka mera or the light sensor that or the static Fixed focus scanning system of the present embodiment ver an industrial type color CCD TV camera such as the Panasonic WV-CD 130 Industrial Co., which uses a 50mm TV lens with a 5mm Extension tube is equipped as it is under the Mar name NAVITRON TM is available. Such a came ra can work with an image capture circuit like the model DT-2803-60 by Data Translation Inc.

The optical imaging can capture the entire characteristic drawing using an area sensor like the above camera and the image capture circuit or alternatively with a linear sensor array which contains a CCD element, the second being Dimension in the scanning of the identification by the Be movement of the object (and the marking) becomes. A CCD element suitable for this purpose is the linear CCD sensor model Thomson-CSF THX 31510 CDZ with 4096 Elements.

For dynamic systems in which the objects carrying the identifications are moved on a conveyor belt or the like, it is advantageous to provide a long optical path between the identifications to be detected and the light sensor. The main reason for this is to reduce the change in the apparent size of the label detected by a spaced sensor. For example, if the length of the optical path is 1.20 m, the image of a label that is only a few centimeters above the conveyor belt is a completely different size than that of a label that is 90 cm above the conveyor belt. However, if an optical path with a length of e.g. B. 6 m is provided, the Abbil applications of such markings are almost the same size, and it can be obtained a uniformly good resolution of the image in the total th detected area. The same applies if an area sensor is used instead of a line sensor. The high resolution is thus achieved in an arrangement as in FIG. 6 through a long optical path.

To focus on license plates at different To enable tall objects, a height sensor will be compels. For this purpose, for example, an ultrasonic sensor can be used be turned, or it is a number of light barriers provided that are interrupted by the object. Correspond An adjustment mechanism with or without a return can then be used guidance (control) are activated to the position of the optical detection elements (e.g. lenses or sensors) to be adjusted continuously.

Fig. 6 is a schematic view of a setting device for a camera, which is used to position the light sensor of the camera according to the height of the detected objects. In FIG. 6, a lens 196 , a coil control 202 , a height sensor 206 and a control therefor are shown. The height sensor 206 can consist of an ultrasonic sensor or a system in which light beams are interrupted by the object. The output signal of the height sensor 206 is fed to a microprocessor 204 which in turn causes the coil control 202 to move a coil 200 <53724 00070 552 001000280000000200012000285915361300040 0002003943563 00004 53605 / BOL <to which a CCD element 198 or another suitable sensor is attached is. A position sensor 208 detects the position of the coil 200 and gives the corresponding output signal to the microprocessor 204, whereby a control loop for detecting and adjusting the position of the coil 200 is formed. The sensor must be able to detect the light reflected from the illuminated label, and it must generate an analog signal which corresponds to the intensity of the reflected properties of the label as they are taken up by the individual pixels of the electro-optical sensor. A suitable light source can, as mentioned, be mounted on a conveyor belt in order to illuminate an area that extends over the entire width of the conveyor belt with light of certain quality and intensity. The light reflected from the identification can be deflected by means of a number of reflectors and only then be detected by the electro-optical sensor's. The purpose of redirecting the light is to create a compact and therefore rigid system. The analog image signal emitted by the sensor is then filtered. The electrical analog signal is used in conjunction with an analog bandpass filter to determine the presence of an acquisition target in the data field. The analog signal is then converted into a digital signal by means of a conventional analog to digital converter in the image capture circuit or some other device. It is possible to provide a digital filter circuit instead of the analog bandpass filter in order to determine the presence of the detection target by comparing the corresponding digital data with the data output by the analog / digital converter. An example of a surface sensor with a CCD element with a number of detectors is the already mentioned color CCD TV camera WV-CD 130 from Panasonic. The analog output signal from this sensor is fed to the DT 2803-60 image acquisition circuit from Data Translation, which contains a 6-bit single-color A / D converter for digitizing the data. By means of a subroutine, the sequential digital output signal of the image capture circuit is stored in a memory in the form of an exact reproduction of the image captured by the optical sensor. The processing of the optically captured image is the most essential process in the exact recovery of the original configuration of the marking and the optical properties (colors) of each hexagon. This processing includes the steps given below, using the known pattern according to which the identifier was originally encoded to decode the information contained in the identifier. The first of these steps is to locate the center of the acquisition target. Before using the mentioned CCD-TV camera and the image capture circuit, as shown in FIG. 10, an initialization program "DTINIT.C" 250 is executed to bring the image capture circuit into a defined standby state and to load the color look-up tables , followed by the program "DTLIVE.C" 255 to activate the image capture circuit. Then the program "DTGRAB.C" causes the image capture circuit to digitize the scene into an image memory with 240 lines and 256 columns, with the pattern is stored as a byte in 6-bit values. Two auxiliary programs "DTSAVE.C" and "DTLOAD.C" cause the screens to be transferred to and from a memory. The first time the image of the license plate is acquired, a conventional analog bandpass filter can be used to identify the optical properties of the concentric rings of the acquisition target. These optical properties are preferably the colors black and white, since the greatest contrast also results in the strongest signal. To find out a fixed pattern for the transitions from black to white and back to black, etc., it is advantageous if a linear scan through the center of the detection target always results in the same frequency response regardless of the orientation of the identifier. The detection target is therefore best formed from concentric rings. The output signal of the sensor is then split up and sent through two detection branches. In one of these branches the total energy of the output signal is measured and in the other the energy with the ring frequency is measured. When comparing the two output signals, the energy from the ring detector must almost coincide with the energy from the total energy detector if the scan was carried out through the center of the acquisition target. In other words, the center of the target is captured when this near perfect match occurs. The file name "FIND.C" lists the processes for generating a digital bandpass filter and for the filter process. In the embodiment with dynamic sampling, an analog bandpass filter or an analog sampling bandpass filter is preferably used in the first filter step. It should be noted that acquisition target location step "FIND.C" 280 of FIG. 10 is designated in FIG. 7 as optional since a hand-held scanner can also be used with the operator appropriately placing the scanner to ensure correct alignment of the sensor. Understandably, however, this takes place much more slowly than with an automatic sensor, and the use of an automatic sensor is essential for high-speed operation. When an automatic (non-handheld) sensor is used, location of the acquisition target is a necessary step in the process. As an alternative to the analog filter described above, a Parks-McClellan algorithm included in the Digital Filter Designs Software for the IBM PC "(Taylor and Stouraitis, Marcel Dekker Inc., New York NY 1987), a digital bandpass filter can be generated. In the present embodiment, a one-dimensional digital band-pass filter was used to filter the normalized digital bit stream using the following filter subroutines. The band to be filtered out is the expected ring frequency. The one-dimensional digital bandpass filter is designed for a sampling rate of 400 pixels per inch (2.54 cm) and a length of 125 pixels (7.9375 mm or 0.3125 inches) and is designed for the dimensions of the printed detection target rings, as shown in 3 is shown. The frequency is thus 300/16 line pairs per inch (2.54 cm), which corresponds to a normalized frequency (where 400 line pairs per inch or 2.54 cm = 1) of 300/16 × 400 or 0.046875. A filter with a pass band is selected that extends up to 5% below this frequency and 15% above this frequency, since distortions in the marking typically result in a reduction in the size of the image and thus an increased frequency. Blocking ranges of 15% below the frequency to 0 and of 25% above the ring frequency to 0.5 (Nyquist limit) were provided. The values for the filters are stored in the "IMPULSE.LUT" file 275 (Fig. 10) for later use, with the first 62 coefficients being omitted because the filter is symmetrical. A corresponding flow chart is shown in FIG. By scanning the bandpass filter at output intervals corresponding to the measured horizontal magnification, a filter with a length of 25 pixels is generated. For example, if the horizontal magnification of the image is 80 pixels per inch or 2.54 cm, every fifth sample of the filter is used (400/80 = 5 pixels). Linear interpolation of adjacent filter samples is performed for non-integer steps. A second, two-dimensional filter of 25 × 25 pixels is also provided. The samples for this two-dimensional filter are based on the Euclidean distance of each point from the center of the filter, which has been adjusted for appropriate horizontal and vertical magnification. Linear interpolation is carried out again for non-integer sampling intervals. The output signal of the one-dimensional filter mentioned is squared and smoothed by means of a recursive low-pass filter of the first order, which results in an exponential section from the history. If the output signal of the smoothing filter exceeds a predetermined threshold, an additional two-dimensional filter step is provided in order to check the presence of the acquisition target and to determine its location precisely. In the first part of the two-dimensional filtering process, a reduced filter size of 10 × 10 pixels is used to make the calculation easier. This filter scans a rectangular area around the location determined by means of the one-dimensional filter. If the maximum of the two-dimensional correlation exceeds a predetermined threshold, the full two-dimensional filtering with the full 25 × 25 pixel filter is finally applied to a small square window around the maximum. If the best result of this filtering again exceeds a predetermined threshold, the center point is established. If one of the thresholds is not exceeded, the program partially "dismantles" the smoothing filter and reverts to one-dimensional scanning. If the one-dimensional scan is completed without the presence of a detection target being detected, the program exits with an error return. The intensities of the reflected light picked up by the optical sensor used may change due to changes in the lighting, the print density, the Re flectivity of the paper, the sensitivity of the camera and other factors including a deterioration in the marking, e.g. by folding, warping, etc. . As an optional (and preferably employed) step, the reflected light detected by the sensor and the corresponding signal passed on to the memory can be normalized in a conventional manner. Using known techniques, the normalization program "NORM.C" 270 shown in FIG. 10 is used in the analysis of the intensity level of the light reflected from the label, as recorded in pixel blocks in the scanner, to the minimum and the determine the maximum intensity of the light reflected by the data field. The digital output signal of the mentioned combination of scanner and image capture circuit is reloaded from the memory to the computer in order to be treated further by means of the normalization program. Using the equation y = mx + b, where the minimum intensity inserted in x yields a value of y = 0 and the maximum intensity inserted in x yields a value of y = 63, the detected intensities of the reflected light are set for each pixel that the blackest black and the whitest white in the stored image are set as the standard and the other color tones of black, white and gray are related to these standards. The normalization process thus facilitates the processing of the captured image. The normalization is carried out by means of a program "NORM.C". Of course, other, more complex normalization methods can also be used. For the following calculations, the saved reproduction of the identification is scaled again in order to obtain an image with the same horizontal and vertical sizes. Again, this is an optional step, but it does facilitate quick and accurate retrieval of the encoded information. The scaling is carried out in order to obtain a uniform horizontal and vertical resolution of the scan of, for example, 150 pixels per inch (2.54 cm) in the image, as it is used in the execution with the static fixed focus scanner. The scaling or downscaling is done by calculating the partial row and column addresses of scans at 1/150 inch based on the known horizontal and vertical magnification. Each point of the new evenly scaled map is then derived from a corresponding set of points from the map that is held in memory. For partial addresses, a bilinear interpolation is used to approximately determine the value of the points. The scaling places the center of the marking in the memory at a known position. The scaled image is finally saved for later use in the browse step. All of the following operations assume that the scaled image of the identifier is centered on a known position in the grid, but it should be noted that the orientation of the identifier is not yet displayed, which will still be tilted with respect to the Sen sors can. The scaling is carried out by means of a subroutine. The next operations are collectively referred to as "two-dimensional clock recovery". These steps are carried out by a program and subroutines entitled "CLOCK.C" 290 (Fig. 10). This process is carried out two-dimensionally on the scaled-back image in order to precisely determine the position of each hexagon in the original data field. The purpose of clock recovery is to determine the scanning positions and to correct the effects caused by warping, curling or tilting of the marking, since the marking does not, for example, need to be completely flat. This is an important part of the process, its application is not limited to hexagonally coded labels, it can also be applied to other labels with a regular two-dimensional grid of, for example, squares, triangles, etc. who the. One-dimensional clock recovery is a well-known process in signal processing. A two-dimensional clock recovery is an extension of this process that can be carried out by anyone skilled in the art. It should be noted that the term "clock recovery" can be somewhat confusing to those of ordinary skill in the art because it does not apply to timing. The first step in executing the clock recovery can be performed using known, nonlinear assignment processes to generate signal components with a clock frequency that are not present in the digitized image output signal of the optical sensor and the image capture circuit. The purpose of the nonlinear mapping is to take the preferably normalized and scaled back mapping that is available at this point in the process and convert it into a two-dimensional nonlinear mapping or map that highlights the transitions between adjacent contrasting hexagons. In the present embodiment, this takes place by means of an assignment using the standard deviations, that is to say a mapping of the standard deviations. This process can also be carried out by filtering with a kernel differentiating the image, of which several types are known, such as LaPlace or Sobel kernels, and determining the absolute value or squaring the results. Corresponding procedures are described in "Digital Image Processing" by Rafael G. Gonzalez and Paul Wintz, Addison Wesley 1977. In the usual standard deviation mapping, the mapping with undifferentiated cell borders is stored in memory. A standard deviation map is then generated to determine the edges of adjacent contrasting hexagons by determining the standard deviation of 3 × 3 groups of pixels (this is different from the groups of 3 × 3 cells) to determine determine the standard deviations of the intensities of the pixels. The calculations for the standard deviation are used to determine the areas of pixels with a certain color (the smallest standard deviation) that represent the interior of a hexagon or the transition between two hexagons of the same color, in contrast to the groups of pixels with a larger standard deviations, which represent transitions from a hexagon of a specific color to an adjacent hexagon of a contrasting color. Since adjacent hexagons are often the same color, a standard deviation map does not fully reflect every hexagon. Missing boundaries or edges between hexagons result from the fact that the process of mapping by means of the standard deviations cannot determine transitions between hexagons of the same color. Clock recovery is therefore intended to generate these missing transitions. The decoding process described can be applied to any of the above-described forms of identification. The coding units for the different geometries can be easily adapted because the optically coded polygon cells are arranged so that the geometric centers of adjacent polygon cells are on the corners of a given two-dimensional grid. If the identifications are read with optical sensors of the named types, the respective geometry or shape of the individual coding units or polygon cells is not determined by the optical sensor. Instead, the sensor merely records the identification with a known number of scans per inch or centimeter and records the intensities of the light reflected in accordance with the optical properties of the respective scanning area. These values are stored in memory for later use. In other words, the electro-optical sensor records the average light intensity scanning surface by scanning surface over the entire label, regardless of whether anything is printed on the label. This is what is meant by the storage of the mapping with transitions from cell to cell which are not differentiated in the memory. For this reason, the decoding process is easy to adapt to designations of various configurations as long as the centers of the polygon coding units have a predetermined distance and a predetermined direction in a two-dimensional lattice. It has been found that a modification of the hexagonal cell-based system, as in the case of polygons which are only substantially in the shape of hexagons, as shown in FIG. 15, only slightly degrades the performance of the System. The use of polygons with poorer packing properties or grids or figures that are partially or not connected at all results in poor performance, but nevertheless sufficient for some applications. At some point, however, the performance of the system naturally falls due to optically no longer resolvable high frequency components of polygonal coding cells of lower order, with poor cell packing and in grids with large gaps between the polygons to an unacceptable, too low capacity for storing and retrieving the information. The suitability of the system depends on the quality of the signal picked up by the electro-optical sensor. By changing the detection system, for example by increasing the number of scans per unit area on the surface of the marking, the signal recorded by the sensor can be improved and the possibilities for information storage and retrieval, in particular of partial and non-contiguous marking configurations, can be improved. The system therefore enables a wide range with regard to the object of the identification, the devices for receiving the optical signal and for processing the optical signal. Polygon cells of either regular or irregular or non-uniform shape can be used as coding units. As long as the distances and directions of the center points of the polygons with respect to neighboring polygons are known, the polygons can lie on any grid and they can be connected completely, partially or not at all. The non-linear mapping techniques, in particular the mapping of the standard deviations described, facilitate the reconstruction of the missing transitions or edges between the polygon cells with the same optical properties using the method described below. In the same way, the missing transitions between polygons and spaces with the same optical properties can be found if the polygons are partially or not connected at all. This is achieved by the fast Fourier transform, filtering and inverse fast Fourier transform described below. An optional technique reduces the computations required to generate the standard deviation map. Typically, eight additions are required to compute the sum of the nine pixels in each of the 3 × 3 pixel blocks. This can be reduced to half if each pixel in the image is replaced by the sum of itself and the pixels immediately to the left and right of it. This requires two additions per pixel. The same process is then carried out on the new image, with the exception that the sum is calculated for the image points immediately above and below it. As a result, two further additions and thus a total of four additions are required. At the end of the process, each pixel is replaced by the sum of itself and its eight immediate neighbors. Standard deviation mapping is preferred for generating a map of the hexagons that corresponds to the original data field but does not contain transitions between hexagons of the same color. The following subroutine, called "windowing", is again optional. Windowing or cut-out is used to reduce the intensities of boundary lines that do not correspond to hexagons. These boundaries occur in two places: the detection target rings and in the mapping of the uncontrolled environment of the tag. A weighting function is used to reduce the intensity of these areas. The person skilled in the art knows how a window is performed before a fast Fourier transform. Under the control of a commercially available program, a two-dimensional, fast Fourier transformation of the digital values is then carried out on the (optionally windowed) mapping of the standard deviations. A computer performs a fast Fourier transformation on the image that was generated in the previous step in order to obtain a two-dimensional representation of the distances, directions and intensities of the transitions between contrasting hexagons. Simply put, the fast Fourier transformation is a measure to determine the distances, directions and intensities of the edges between hexagons, as far as known. The regular distances and directions of the boundaries of the hexagons result in points in the transformation area with a high energy level. The brightest point is in the transformation plane at 0.0 corresponding to the direct current component in the figure. The six points surrounding the center point represent the distances, directions and intensities of the edges between the hexagons. By performing a fast Fourier transform on the digital data that correspond to the nonlinearly mapped, he grasped identification, a two-dimensional representation of the distances can also , Directions and intensities of the intersections of contrasting polygons are calculated as they were identified in the previous mapping step via the standard deviations. The distances and directions of the polygon boundaries then result in certain points of high energy in the transformation area. The number of high energy points surrounding the zero point at 0.0 of the transformation plane depends on the geometry of the respective polygon cell that is used to construct the marking. In the case of hexagons, these points surrounding the center point represent the distances, directions and intensities of the edges between polygons or between polygons and spaces if the marking is made up of only partially or non-connected elements. Since the mapping is evaluated as real (and not complex), the transformation area around the origin is point-symmetrical. Therefore, only half of the transformation area needs to be calculated, which reduces the computation time accordingly. This also reduces the effort involved in the following mapping filtering and the inverse, fast Fourier transformation. The program with a fast Fourier transform used in connection with the static fixed focus system is, for example, the subroutine R2DFFT of the 87FFT-2 package from Microway Inc., Kingston, Massachusetts. To reconstruct the complete outlines of all hexagons in the image area, filtering is carried out using the transformed digital data. This eliminates those points in the transformation area that do not correspond to the desired distances and directions of boundaries of the hexagons that were obtained in the mapping step with the standard deviations. There are six distinctive points in the transformation area due to the hexagonal honeycomb structure of the marking. Only three of these points are actually identified in the transformation area, since the mapping around the origin is point-symmetrical and the second three points can be derived from the first three. The filtering is preferably carried out in three steps in order to eliminate those transitions from the mapping obtained via the standard deviations which are too far apart or too close together and / or point in the wrong direction. First there is a high-pass filtering in which all points within a certain circle around the origin of the transformation area are set to zero, but without the six distinctive points that are arranged in the graphic transformation area at a distance outside the origin in the shape of a hexagon. These points correspond to distances that are greater than the distances in the hexagons and thus contain information about the missing transitions in the image of the marking. In order to recover missing transitions in the image of the label, it is necessary to eliminate the information about the missing transitions in the Fourier transform domain. Next, all points outside a certain radius around the six prominent points in the transformation area are set to zero. These points correspond to disruptive transitions that are too close to one another. Along with the first operation, this operation will create a ring of remaining points. The creation of this ring is equivalent to performing spatial bandpass filtering. The inner and outer radius of this circular ring is determined by means of the expected distance between the hexagonal outlines. Since it can be expected that in the example described the "diameter" of the hexagons is equal to 5 pixels and since for a transformation length of 256 pixels, the tips of the hexagons in the transformation area are equal to 256/5 = 51.2 pixels from the center point should, a ring with an inner radius of 45 pixels and an outer radius of 80 pixels corresponds to the hexagon diameters used from 3.2 to 5.69 pixels. A filter is used that allows higher frequencies to pass, since deformations of the identification, such as warping and tilting, cause the image to be reduced in size. After this spatial bandpass filtering has been carried out, a circular ring remains with six distinctive points, each of which is at the same angular distance from the center (zero point) of the transformation area. Directional filtering is also carried out to completely eliminate unwanted information in the transformation area. Every point with too great an angular distance from the prominent points in the transformation area is set to zero. This has the consequence that in the image area all edges are eliminated who do not run in one of the three directions that occur in the hexagonal honeycomb pattern. To carry out this directional filtering, it is necessary to find the most prominent point that has remained after the spatial bandpass filtering. It is assumed that this point is one of the six distinctive points in the transformation area that correspond to the tips of a hexagon. In the transformation area there are also five other prominent points with the same distance from the center point and the same angular distance of multiples of 60 degrees. Therefore, all other points with angular distances greater than 10 degrees from any of these points are eliminated. There remain six wedge-shaped pieces of the ring. This directional filtering eliminates information about incorrect distances or directions in the image area. The elimination of this incorrect spacing information enables the full outline of each hexagon in the image area to be recovered. The filters mentioned are carried out using subroutines. The filtering process described above for contiguous hexagons must be changed if other two-dimensional grids are specified for the identification. However, only minor modifications are required to accommodate other configurations. After the individual polygon cells have been defined, the angular distances between the boundaries and the number of pages and their lengths are known. Next, it is necessary to determine the relationships between neighboring polygons, such as whether they are contiguous, partially contiguous, or not. The geometric grid according to which the center points of the polygons are arranged must also be defined. Since the geometry of the marking is given in each case, the appropriate filter for filtering out the energy points in the transformation area can be easily created so that only the brightest points are processed by the subroutine for the inverse fast Fourier transformation according to the appropriate distances and directions of the polygon boundaries become. With regard to the filters produced, it is necessary to produce an appropriately sized spatial bandpass filter on the basis of the predetermined spacing of the encoded polygon cells. Then it is beneficial to create a directional filter to filter out the energy points that are not the most prominent points that correspond to the axes of the given two-dimensional grid. This eliminates the information about incorrect distances or directions of the cells in the image area and possibly the spaces. By eliminating this incorrect information, a complete grid of the center points of the polygon cells can be obtained in the image area by means of the inverse fast Fourier transform. In order to return to the image area and thereby restore the image of the outlines of the adjacent hexagons of the data field, a two-dimensional, fast Fourier transformation is carried out on the filtered data of the transformation area. This inverse Fourier transformation is carried out by means of a corresponding standard subroutine that is contained in the 87FFT-2 package from Microwave Inc. After performing the inverse Fourier transform, the outlines of each hexagon in the image area are restored. In this new image, the centers of the hexagons have great value. The actual values for the points at the centers of the hexagons depend on how many edges are in the area. More edges create a higher energy level at the allowed frequencies and therefore points with larger values. Fewer borders result in points with smaller values. The values of the points are a good measure of the value of the confidence level in clock recovery at any given point. The image of the hexagons has now been recovered, but it is still necessary to determine their orientation. The hexagonal honeycomb pattern has three axes with an angular distance of 60 degrees. The direction of these axes is determined by the brightest points in the transformation area after the spatial bandpass filtering. It is then possible to find out which of these three axes is the main axis. This step is optional. If the step is not carried out, the identifier is to be decoded three times using each of the three axes, only one axis giving information with a meaning. The main axis is chosen arbitrarily as the axis of the hexagons which runs parallel on two sides of the marking, as described in connection with FIG. If the boundaries of the square label are determined based on knowledge of the major axis, then most of the energy in the restored pattern of the hexagonal outlines will be within these boundaries of the square. To determine the major axis, it is first assumed that any of the three axes can be the major axis. For each of the axes, the outline of the resulting square identifier is then tentatively determined and determined how much of the total clock recovery energy from the digi tal energy data obtained from the subroutine for the inverse Fourier transform lies within that square. The correct axis is then the one that gives the most energy. The angle of this axis is then saved for initialization and other searches. It is not yet known whether the recorded angle corresponds to the correct direction or is 180 degrees wrong. It will be understood that there is no need to determine all three indicia, since it is not necessary to determine the energy in the areas common to all three squares. A program entitled "SEARCH.C" 300 (Fig. 10) combines the transformed and restored information about the hexagon centers with the stored intensity levels of the original image to determine the value for the gray tone of each hexagon. The search is carried out so that the likelihood of "getting lost" during the search is minimal. The end result is a matrix of the gray scale values for each hexagon in the data field. In the first part of this program, four important information fields are established. The CVAL (clock value) field stores a value for the quality of the recovered clock signal for each hexagon, while the GVAL field contains the gray tone values (0-63) at the center of each hexagon. The row and column positions of the center points of each hexagon are stored in the remaining fields IVAL and JVAL. The expected horizontal and vertical distances from the center of a hexagon to the centers of the surrounding hexagons are calculated from the certain main axis angle and the known from the hexagons (5 pixels). After these calculations, the SEARCH.C program works on the clock recovery signal, which is passed from a memory, and on the scaled-down image of the identifier, which is also obtained from a memory. The primary purpose of the initialization subroutine is to merge and compress the information from these two sources and to generate a data matrix containing the gray values for each hexagon. The initialization step of the search is limited to a square around the center of the label with a side length of approximately one third of the side length of the label. Within this area, a good starting point is the point with the greatest value in the recovered clock signal field. Then the position of this starting point is determined relative to the center of the marking. This starting point is a point where the clock signal is strong and clear, and also a point which is relatively close to the center of the mark. A clear signal is beneficial to ensure that the search begins with a valid hexagon center, and the location of this point close to the center of the marking ensures that its position can definitely turn without distortions or canting conditions a major influence exercise. A measure of the quality of a point in the clock recovery pattern is the value for the point minus the values for the eight surrounding points. The rectangular coordinates of the starting point are converted into polar coordinates, the polar coordinates are set with respect to the previously determined main axis angle and the result is converted back into the rectangular shape. These coordinates are scaled according to the expected line spacing (4.5 pixels) and column spacing (5 pixels) in order to arrive at the insertion positions in the hexagonal matrix. The clock quality, gray tones and positions that correspond to the starting hexagon are then inserted in the corresponding fields CVAL, GVAL, IVAL and JVAL. The following main search loop leads to the localization of the centers of the remaining hexagons. The loop ends when the expected number of hexagons has been localized. The order of the search for the centers of the hexagons is extremely important. The increased reliability of the decoding process in the event of malfunctions in the identification results from the special search technique used, which is explained below. Each iteration of the search loop begins with the renewed call of the position of the clock recovery point with the highest value, whose neighbors have not yet been searched for their highest value. From this known point the search is expanded by a hexagon for each of the six directions. The search pattern is thus built along a path that leads from better to worse recovered clock quality. If there is a bad area in the recovered clock, for example in the center of the marking or in a disturbed area, the search algorithm runs around it and not through the middle. By bypassing the bad areas and keeping them until last, the likelihood of getting lost on the grid is greatly reduced. Since getting lost is just as bad as reading a wrong shade of gray, this property of the search algorithm is extremely important. A subroutine is used to find the neighbors of the best clock values found with the main loop. The subroutine runs through each loop six times, once for each adjacent hexagon of the hexagon currently under consideration. First the position of the neighbor is calculated. If that neighbor is outside the bounds of the label, the loop iteration terminates. If not, the neighbor is checked to see whether it has already been detected from another direction. If the neighbor has already been detected once, the iteration of the loop is also terminated, since the algorithm rates earlier search runs higher than later ones. If the neighbor passes these tests, the expected position of the neighbor's center point in the clock recovery pattern is calculated. At this point, a gradient search is performed for the clock signal with the largest value. The eight pixels surrounding the position found again are searched to see whether a higher clock value is found. If so, the eight neighbors of the best neighboring point are checked to see whether an even better value is found. This gradient search results in a degree of adaptation that is absolutely necessary when distorted and tilted identifiers are to be read. The subroutine then moves to the next neighbor or returns if all neighbors have already been checked. As described above, the reconstructed grid now contains information about the geometric centers of the polygon cells as a result of the data transformation. This grid has more energy in areas where there were originally more contrasting interfaces. The center points lie on the predetermined two-dimensional grid with a given number of equally or not equally spaced axes. The information about the spatial relationships of the axes of the specified grid can be used to determine the orientation of the main axis. It should be noted, however, that the algorithm can be changed in such a way that the actual geometry of the two-dimensional grid is determined during the decoding process and that the filter scheme is determined from this determination, so that the so-called main axis of the marking, i.e. the axis of the two-dimensional Grid, which is parallel to two sides of a square label, and the necessary coordinates for the search subroutine are generated. Whether the geometry of the identifier is determined by such an optional step as above or whether the decoding process is simply entered by suitable modifications of the two-dimensional clock recovery process, the various types of identifiers can always be easily processed. The number of axes on which the center points of the individual polygon cells lie and their respective orientation can be used to determine the main axis. The major axis of the given two-dimensional grid can thus be determined without carrying out the above empirical analysis method. In the hexagonal arrangement according to the preferred embodiment, the information from the determination of the main axis and the known distances between the polygons can be used to calculate the expected horizontal and vertical distances from the center of a polygon to the centers of the surrounding polygons. After such calculations and after making the necessary changes to the search subroutine, the search, including the initialization step and the main search loop, is carried out for the respective identification configuration. After the completion of the subroutine, the location of the current center point is marked so that it is not searched again. The aim is to delete this position as a candidate for a neighbor search. With each iteration of the loop, 0 to 6 new candidates are added and one candidate is deleted. An effective implementation can use a data structure that includes the candidates in order of size when performing the insert and delete operations. One of the possible structures is the so-called priority queue (cf. "The Design and Analysis of Computer Algorithms" by Aho, Hopcroft and Ullman, Addison Wesley 1974). It is known that a linear search algorithm ^{requires n 2} operations, while an efficiently executed priority queue with a symmetrical tree or heap structure requires n logn operations. A memory sorting with n operations based search algorithm can also be used if the recovered clock values are scaled and reduced to a small range of integers. After the main search loop is complete, the location of all center points of all hexagons is determined and the gray tones of the center points of all hexagons that have been saved are fully inserted. The next step is a threshold value determination of the digitized gray tone values in the range from 0 to 63 for the discrete values of, for example, black, gray and white. This is done by building a histogram for the intensity values of the hexagonal center points of the marking. Slice levels can be determined by detecting dips in the histogram. After a threshold value determination of the discrete levels, two disturbances can still be present. On the one hand, the array can be shifted from the center. This can be the case if the initial search step does not exactly indicate the position of the qualitatively best clock signal relative to the center of the identification. The second possibility is that the entire label was read the wrong way round, since the main axis with respect to the 180-degree angle is not clear. A subroutine determines whether the label is off-center. When the label is correctly positioned, the coordinates of the middle row run through the center of the label. In order to determine whether there is a vertical positioning error, lines above the assumed center line are checked to see whether they form a line which passes closer to the marking center point. If a line above or below is closer to the center than the assumed middle line, a corresponding shift up or down takes place. If the left adjustment of the short lines has been carried out incorrectly, this is corrected by shifting the short lines one position to the right. Horizontal positioning errors and an "upside-down" reading are checked using information contained on the label as so-called coarse grid information. The information is divided into groups of 3 × 3 cells as previously described. Since the identification in the given example is a grid of 33 rows and 30 columns, these groups form an 11 × 10 grid. The lower middle hexagon of each complete group of 3 × 3 cells has a particular property that is established during coding. As described in connection with FIG. 4, this hexagon has a fixed transition on each side. For example, if the lower middle hexagon is black, the lower left and right hexagons are either gray or white. A subroutine perceives these transitional properties in order to eliminate the two possible faults. To do this, an array is first generated in which each element of the array shows whether there is a transition between two horizontally adjacent hexagons. Then the array is checked for each of the nine hypothetical slices of the coarse grid arranged as a 3 × 3 pattern around the expected slice of 0. One of these cuts will show a better match between the actual and expected transitions, and the location of this cut will be determined. The same hypothesis is then tested, assuming that the label was read upside down. If the marking is simply reversed, that is, if the upper rows are interchanged with the lower rows and the higher columns with the lower columns, the result of the intersection is also reversed. However, when correcting a flipped number, an important transformation must also be carried out. While reading, the short lines (length = 29) are directed to the left; therefore, if the label is reversed, these lines must be aligned to the right. This process has the consequence that the result of the assumed cuts is different from a simple reversal. The best result of the cutting tests will be better than any previous test if the label was actually read upside down. After determining whether the label was read upside down and whether the cuts are in the absolutely correct position, the matrix of the label can be decoded. With the correct definition of the image and the sections, the image processing is finished and the data decoding operations begin. A program "RD.LABEL.C" 182 (FIG. 9) reads out the file generated by the search program and generates a bit string file having, in the present embodiment, 1292 bits. For this purpose, a subroutine "CELL DEC.C" 183 is used to hide unusable hexagons and for the decoding process, which is a reverse of the encoding process. The first step in decoding is to generate a bit sequence from the information represented by the hexagons using a hexagon-to-bitmap mapping process which is an inverse of the hexagons-to-hexagon mapping process used for encoding was used. The program then divides the bit sequence into a bit sequence for the high priority information and a bit sequence for the low priority information or as many bit sequences as were used during the coding of the identifier. It is then necessary to perform error correction on each bit string using the error coding techniques used in coding the tag. For example, if a Reed-Solomon code was used, error correction in the bit sequence generated by the search program results in an output signal having the same format as the encoding input file described above. Error correction can be carried out with the following procedures (see "Theory and Practice of Error Control Codes", see above): 1. Calculating syndromes; 2. Computing the error localization polynomial using the Berlekamp-Massey algorithm; 3. Calculating the location of the faults using a Chien search; and4. Calculate the size of the errors using the Forney algorithm. The last step is only carried out if a correctable number of errors were found in steps 2 and 3. The number of errors found is also calculated. If an uncorrectable number of errors is found or there is an error within the padding (see above), a flag is set. The error procedure used is called "ERRDEC.C" 184 (FIG. 9). In order to output the decoded information on a computer terminal, the program "TEXTOUT.C" 185 (FIG. 9) can be used.

Claims (30)

1. Decoding method for an electro-optically detected identification on which information is encoded by means of a number of polygons, the polygons with their center points are arranged on a two-dimensional grid and one of at least two optical properties aufwei sen, with the following steps :
Locate the polygons and
Determining their optical properties,
characterized,
that in order to locate the polygons, the area of the identification in which the information is encoded is transformed into a frequency space,
that the periodicity of the two-dimensional grid is determined in the frequency space in order to obtain the geometric locations of the polygons, and
that the respective optical Ei property of the polygons is assigned to the geometric locations. 2. The method according to claim 1, characterized in that the transformation into the frequency space by means of a two-di dimensional Fourier transform is performed. 3. The method according to claim 1 or 2, characterized net that the polygons are not contiguous or partially are connected and that between the polygons between rooms each with one of the mentioned two optical properties properties are available. 4. The method according to any one of claims 1 or 2, characterized ge indicates that the polygons are arranged contiguously are. 5. The method according to any one of claims 1 to 4, characterized net that the two-dimensional grid is a hexagonal grid is. 6. The method according to any one of claims 1 to 5, characterized net that the polygons are regular polygons. 7. The method according to any one of claims 1 to 5, characterized net that the polygons are irregular polygons. 8. The method according to any one of claims 1 to 5, characterized net that the polygons are essentially more regular in shape Have hexagons. 9. The method according to any one of claims 1 to 5, characterized net that the polygons are hexagons that are next to each other in egg are arranged in a honeycomb pattern. 10. The method according to claim 9, characterized by the Step of determining the major axis of the hexagons above a first determination of all axes of the hexagons and the following determination of which of these axes is a particular Has relationship to a limit of the figure. 11. The method according to any one of claims 1 to 10, characterized in that the determination of the periodicity comprises:

• i) performing a non-linear assignment operation on the digital signals in order to determine transitions between adjacent polygons or between polygons and spaces, each with different optical properties;
• ii) performing a Fourier transform on the non-linearly mapped digital signals in order to obtain a two-dimensional representation which corresponds to the directions, distances and intensities of the transitions in the optical properties of the polygons;
• iii) filtering the transformed, non-linearly mapped digital signals in order to eliminate incorrect directions and distances for the optical transitions of the polygons; and
• iv) performing an inverse Fourier transform on the filtered, transformed, nonlinearly formed digital signals to obtain the recovered grid.

12. The method according to claim 11, characterized by a Step of windowing the non-linear associated digita len signals before performing the Fourier transform, to the intensities of the optical properties that are affected by the electro-optical sensor were detected and not with the Polygons are linked to decrease. 13. The method according to claim 11, characterized in that the step (i) generating a two-dimensional map of the transitions between neighboring polygons or between Polygons and spaces, each with a different optical Properties by calculating the standard deviation of the optical properties of the through each pixel and the these surrounding pixels of the electro-optical sensor recorded figure, with larger values for the standard deviation of the transition areas at the Interfaces of the polygons correspond. 14. The method according to claim 13, characterized in that at the center of each polygon, the lo is calibrated, a threshold for the detected Figure is executed to the respective optical own to determine the shaft of the polygons. 15. The method according to claim 14, characterized in that the threshold value formation for the captured image by the Generation of histograms is carried out, which the respective represent the optical properties of the polygons. 16. The method according to any one of claims 11 to 15, characterized in that the localization step includes:

• i) an initialization step which searches the coordinates and intensities of the optical properties obtained via the two-dimensional grid within a certain range of the number of polygons in order to identify the position with the greatest intensity; and
• ii) a step with a search continuation loop in which the coordinates and intensities of the optical properties recovered via the two-dimensional grid are searched over the entire image, starting from the position with the greatest intensity after step (i) and each adjacent position is searched for the next greater intensity, each identified posi tion corresponds to the center of a polygon.

17. The method according to any one of claims 1 to 16, characterized in that the image captured by the electro-optical sensor is a capture solution target from a number of concentric rings different, alternating optical properties tet, and that the first step of the process is the locali sizing the acquisition target by filtering the digital Signals and correlating the digital signals with one Signal of a certain frequency. 18. The method according to any one of claims 1 to 17, characterized by a Normalization of the captured image of the identification predetermined values for each of the predetermined optical eggs properties of the mapping before execution of the transformation step. 19. The method according to any one of claims 1 to 18, characterized by a Rescaling of the image before executing the transformation step, to create a figure with the same horizontal and ver to obtain vertical enlargements. 20. Device for performing the method according to one of claims 1 to 19, with:
a device for locating the polygons and determining their optical properties,
characterized,
that said device for locating the poly gone and determining their optical properties
a device for transforming the area of the identification in which the information is encoded into a frequency space,
a device for determining the periodicity of the two-dimensional grid in the frequency space in order to obtain the geometric locations of the polygons, and
has a device for assigning the respective optical property of the polygons to the geometric locations. 21. The device according to claim 20, characterized by a Means for determining the main axis of the polygons over a first determination of all axes of the polygons and the fol the determination of which of these axes has a particular be has a relationship to a limitation of the figure. 22. Apparatus according to claim 20 or 21, characterized by

• a) a device for performing a non-linear assignment operation on the digital signals in order to determine transitions between adjacent polygons or between polygons and spaces, each having different optical properties;
• b) means for performing a Fourier transform on the non-linearly mapped digital signals in order to obtain a two-dimensional representation which corresponds to the directions, distances and intensities of the transitions in the optical properties of the polygons;
• c) means for filtering the transformed, non-linearly mapped digital signals in order to eliminate incorrect directions and distances for the optical transitions of the polygons; and
• d) means for performing an inverse Fourier transform on the filtered, transformed, non-linearly mapped digital signals in order to obtain the recovered grating.

23. The device according to claim 22, characterized by means for windowing the non-linear associated digital signals before performing the Fourier transform tion to the intensities of the optical properties that were detected by the electro-optical sensor and which were not associated with the polygons. 24. Apparatus according to claim 22 or 23, characterized in that that the mapping device is a two-dimensional map of the Transitions between neighboring polygons or between polygons and spaces, each with different optical properties by calculating the standard deviation the optical properties of each pixel and the Ab recorded these surrounding pixels generated, with larger values for the standard image softening the transition areas at the interfaces of the Polygons correspond. 25. The device according to claim 24, characterized by a Establishment that is at the center of every polygon that is located by the localization device, a threshold value formation for the captured image carries out to the to determine the respective optical property of the polygons. 26. The device according to claim 25, characterized in that that the device for threshold value formation a Einrich for generating histograms that contain the represent the respective optical properties of the polygons. 27. Device according to one of claims 22 to 26, characterized in that the localization device contains

• i) initialization means for searching the two-dimensional retrieved grid within a predetermined area to determine the position of greatest intensity; and
• ii) a search continuation device which searches the two-dimensional retrieved grid over the entire grid area, starting with the position of greatest intensity from step (i) and searching the adjacent positions to the next greatest intensity, each position identified corresponding to the center of a polygon.

28. Device according to one of claims 22 to 27, characterized by a Device for normalizing the captured image of the Marking on predetermined values for each of the given optical properties of the image before execution the nonlinear assignment operation. 29. Device according to one of claims 22 to 28, characterized by a Facility for rescaling the image before execution the nonlinear mapping operation to map with to get the same horizontal and vertical enlargements. 30. Device according to one of claims 20 to 29, characterized in that that the image captured by the electro-optical sensor Acquisition target made up of a number of concentric rings different, alternating optical properties bein hold, and that a device for localizing the Acquisition target by filtering the digital signals and Determine correlating the digital signals with a signal ter frequency is provided.