JP4267965B2 - Bar code reader - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to image reading, and more particularly, to a bar code reader for reading bar codes.
[0002]
[Prior art]
While the digitization of business is progressing and the number of electronic documents in circulation is increasing, the document display quality of displays such as CRT and LCD is still inferior to the print quality on paper, so it is created electronically Opportunities to print the printed document on paper as a paper document are increasing. In order to effectively use the handwriting work performed on these paper documents, a means for digitizing the added information is required. However, the handwriting input devices currently used are low in functionality and thus are easy to use. It ’s bad.
[0003]
As a handwriting input device, for example, there are devices called a tablet and a digitizer, which perform handwriting input to a computer using a plate-shaped sensor and a pen-type device. In order to reflect the handwritten work on the paper document in the electronic document, there is a problem.
[0004]
The problem is that, for example, when an electronic document is written on printed paper, it is desirable to insert the original electronic document, but it is impossible to insert the electronic document. Is mentioned.
[0005]
Even if a scanner device is used, a paper document can be converted into an electronic document. However, in this case as well, there is a problem in reflecting the handwriting operation on the paper document in the electronic document. For example, when a paper document on which an electronic document is printed is digitized again with a scanner, an image document is only created as another electronic document, even though the original electronic document created with word processing software exists. Further, the image document is also imaged by mixing the original electronic document and the additional information, and it is impossible to insert the additional information into the original electronic document.
[0006]
These conventional devices only have a function for acquiring the retouched coordinates and a function for acquiring the image, and cannot identify what the original electronic document printed on the paper looks like. Since the electronic document into which the coordinates are to be inserted is unknown, the above-described problem occurs.
[0007]
In order to solve these problems, Patent Document 1 and Patent Document 3 solve the problem by the following method. First, when printing an electronically created document, document identification information for specifying the original electronic document is simultaneously printed with a pattern such as a barcode that can be easily identified by a machine. Here, the document identification information is information indicating the storage location, file name, page number, etc. of the original electronic file printed on the paper document, or information for managing them. For example, “// C: \ MyDocument ¥ tmp. doc, pp1 ”and the like.
[0008]
Next, at the time of handwriting input to a printed paper document, document identification information is acquired together with the added coordinate information. Furthermore, when inserting additional information into an electronic document, according to the document identification information, identify what electronic document the printed paper document has been printed on, and insert the additional information into an appropriate electronic document. It is possible.
[0009]
In particular, in Patent Document 2, a plurality of paper documents are stacked and placed on a handwriting input device, and can be automatically identified while being turned. Reflecting on the electronic document, it is possible to more efficiently implement the business by using the electronic document and the paper document mutually.
[0010]
FIG. 22 shows a schematic diagram of a conventional handwritten information input apparatus. This handwritten information input device is a device that acquires handwritten information performed on a plurality of paper documents. The handwritten information input device includes a handwritten input unit that can input handwritten information, and a document identification information reading device that can read document identification information assigned to each of a plurality of paper documents as a unique number.
[0011]
When using, the document identification information is read while turning a plurality of stacked paper documents, and the acquired handwritten information is inserted into an appropriate electronic document.
[0012]
At this time, a document identification information reading device constituted by a two-dimensional code reader, a barcode reader, or the like for use while turning a paper document is installed beside the paper document and displayed on the paper document by a barcode or the like. It is necessary to read the document identification information from an angle.
[0013]
At this time, the imaging surface of the code reader is placed so as to be orthogonal to the optical axis in order to avoid darkening the image due to the aberration of the optical system. Will be. At this time, a sufficient amount of light can be obtained, and if it is an optical system having a deep depth of field, it is possible to obtain a clear image in a wide range. As with the paintings that are drawn, when a rectangular or square two-dimensional code is read with an oblique optical system, it is transformed into a trapezoid. A phenomenon opposite to this phenomenon can be seen, for example, in the case where an image by a projector is projected obliquely on a wall surface, and a rectangular figure is originally transformed into a trapezoid.
[0014]
That is, in the oblique optical system, the front side is large and the pattern density is rough, and the back side is small and the pattern density is dense. On the other hand, as a two-dimensional image sensor used in the image pickup system, a CCD and a C-MOS sensor are generally used, but fine image sensors are uniformly distributed in a matrix. When such a uniformly distributed image sensor is used to read a two-dimensional code having a different pattern density from an oblique direction, the image is captured in such a size that a portion to be imaged with the highest density can be read. In other words, one element reads one block in the two-dimensional code in the small moving image on the back side, while one pattern (block) appears in the large image on the near side. Multiple elements are reading. In this way, in the portion having a low density on the near side, since a plurality of elements express the same block and the same information, it is difficult to efficiently express the information.
[0015]
On the other hand, as the number of electronic documents increases, the number of paper documents to be printed also increases. Therefore, a code required for document identification is required to have a larger capacity. Due to restrictions on the size of the document identification information reading device 003 and the area where the code is printed, for example, when a 6-digit Code39 format is used as a one-dimensional code and used for document identification, the amount of information that can be expressed is 36 ^ 6 = 2.1 billion. However, as described above, the amount of paper documents printed is increasing, which is insufficient to identify all paper documents printed in normal business operations. On the other hand, in the two-dimensional code, for example, in the case of a two-dimensional code having 12 × 6 cells (black and white minimum unit), if the error correction code is 32 bits, the amount of information that can be expressed is 2 ^ 40 = 1 trillion. If the error correction code is 16 bits, 2 ^ 56 = 7.2 K. This is much larger than the case of Code 39 above, and is an amount of information sufficient to identify a normal paper document.
[0016]
As described above, in the conventional handwritten character input device, a barcode is used as a machine-readable pattern (code) used for identifying a paper document, and because of its limited capacity, a large amount of electronic documents And paper documents were difficult to use in cooperation. Under these circumstances, in order to increase the amount of identifiable documents in a handwriting input device, development of a two-dimensional code that can be easily read by an optical system that is read from an oblique direction is required.
[0017]
By the way, when actually using the code, it is necessary to read it quickly and with high accuracy before writing begins. Therefore, the amount of image data processed is reduced as much as possible, and an accurate algorithm is available even if the image quality is poor. Will be needed.
[0018]
In Patent Document 2, there is a description of a two-dimensional code having two different cell sizes. The purpose is to fix a hand-held bar code reader and a two-dimensional code affixed to an article flowing on a belt conveyor. It was invented so that it can be read by both the bar code reader and it is not assumed to be read obliquely as in the present invention.
[0019]
[Patent Document 1]
JP-A-9-91083
[0020]
[Patent Document 2]
JP 11-328301 A
[0021]
[Patent Document 3]
JP 2000-112646 A
[0022]
[Problems to be solved by the invention]
The present invention has been made in view of such problems, and an object of the present invention is to provide a high-speed and high-accuracy two-dimensional code barcode reader.
[0023]
[Means for Solving the Problems]
In order to solve the above-mentioned problem, the present invention acquires a projected image on which the barcode is projected by photographing a barcode having a rectangular frame, and reads the information represented by the barcode. In the projection image The black pixel is detected by performing oblique scanning from the four corners of the image including the pixel, the predetermined number of pixels are tracked in the predetermined direction from the detected black pixel, and the detection is performed when all the tracked pixels are black pixels. Black pixels, Vertex candidates for a frame of a barcode image that is a barcode photographed in the projected image As A vertex candidate detecting means for detecting; a code frame detecting means for detecting a frame of the barcode image and determining a vertex of the barcode image based on the vertex candidate; a vertex of the barcode frame; Projection conversion coefficient calculation means for calculating a projection conversion coefficient based on the vertex of the frame of the barcode image; and data sampling means for acquiring information represented by the barcode from the barcode image using the projection conversion coefficient. It is characterized by having.
[0026]
In order to solve the above-described problem, the present invention provides the code frame detection unit, wherein the barcode pixel is a pixel on the line segment among pixels on a line segment connecting pixels determined for each vertex candidate based on each vertex candidate. When the ratio occupied by is greater than or equal to a predetermined ratio, it is determined that a barcode frame photographed in the barcode image is detected.
[0027]
In order to solve the above-mentioned problem, the present invention provides that the inside of the frame of the barcode has information in one of two colors for the data sampling means to acquire information. A plurality of information areas to be represented, wherein the data sampling unit obtains colors of a plurality of pixels in the information area, and obtains information represented by the information area in accordance with a ratio occupied by one of the obtained colors It is characterized by doing.
[0028]
In order to solve the above-mentioned problem, the present invention provides the data sampling unit, wherein the position of the information area in the barcode and the position of the information area photographed in the projected image are converted to the projective transformation coefficient. It is characterized by making it correspond using.
[0029]
In order to solve the above problem, the present invention further includes a finder detection unit that detects pattern information represented by the information area that is provided in the barcode and is arranged at a predetermined position. To do.
[0030]
In order to solve the above problem, the present invention is characterized by further comprising error correction means for correcting an error when the information acquired by the data sampling means has a correctable error.
[0031]
In order to solve the above-mentioned problem, the present invention further includes a format conversion unit that converts information acquired by the data sampling unit into a numerical value.
[0032]
As described above, according to the present invention, it is possible to provide a high-speed and high-accuracy two-dimensional code barcode reader.
[0033]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the barcode reader in the present embodiment may be provided, for example, in the document identification information reader of FIG. 22 or may be a single unit.
[0034]
First, an image read by the barcode reader will be described. First, the two-dimensional code shown in FIG. 1 will be described. As shown in the figure, the two-dimensional code is surrounded by a black frame, and the cell which is the smallest unit and information area representing 1-bit information in white or black is 72 as shown in FIG. Are arranged. Therefore, this two-dimensional code can express 72-bit information.
[0035]
When this two-dimensional code is read obliquely from above, the code image obtained by the bar code reader is reduced to a trapezoidal shape by reducing the lower part where the cells are large. The code image of the entire two-dimensional code is distorted in this way, but each cell in the code image photographed by the barcode reader occupies a plurality of pixels of the image sensor, and the area thereof is approximately equal regardless of the position of the cell.
[0036]
As described above, the image sensor of the barcode reader provided to read the code image obliquely from above with a two-dimensional code in which the size of the upper cell is small and the size of the lower cell is large is Stable shooting of information becomes possible.
[0037]
Another example of the two-dimensional code is a trapezoidal code (plan view) shown in FIG. This trapezoidal code is a two-dimensional code in which the size of the cell increases in accordance with the distance between the imaging element and each cell when the barcode reader reads from obliquely above.
[0038]
In the case of this trapezoidal code, 12 cells are arranged in each row and there are 6 rows, so the total number of cells is 72. Since the total number of cells is equal to the total number of cells of the two-dimensional code in FIG. 1, the number of bits that can be expressed by the trapezoid code is equal to the number of bits that can be expressed by the two-dimensional code of FIG. Incidentally, the 72-bit data is arranged as shown in FIG. 4 showing the arrangement of the data.
[0039]
As another example of the two-dimensional code, a two-dimensional code obtained by projective transformation of a rectangular two-dimensional code 50 as shown in FIG. 5 and transformed into a trapezoidal two-dimensional code 51 can be used. In this case, an image of the code obtained when the image is taken obliquely from above is obtained that is close to the two-dimensional code 50. The amount of information that can be expressed by the two-dimensional code 51 is 72 bits.
[0040]
One thing common to the three two-dimensional codes described above is that the size of the entire code is horizontally long. The reason for this is that if the cell is vertically long, the vertical length of the cell becomes large, the overall size of the code becomes very large, and the advantage of the two-dimensional code that it is possible to express a large amount of data with a small size is lost. is there.
[0041]
When viewed from the bar code reader, the distance from the code is larger in the horizontal direction than in the vertical direction, so the cells far from the bar code reader are rectangular with a large aspect ratio. Is appropriate. Therefore, in the example of FIGS. 3 and 5, by making the change in the vertical length larger than the change in the horizontal length of the cell, each cell of the code is close to a square in the code image taken by the barcode reader. It becomes a shape.
[0042]
The two-dimensional code described above is an image read by the barcode reader in the present embodiment. Next, a two-dimensional code creation apparatus that creates the above-described two-dimensional code will be described with reference to FIG.
[0043]
The two-dimensional code creation device includes a format conversion unit 20, an error correction unit 21, and a code image creation unit 23.
[0044]
The format conversion unit 20 converts the input numeric character string into integer 56-bit data that is normally handled in the computer.
[0045]
The error correction unit 21 adds 16 bits of an error correction code to the data converted into the integer type 56-bit data.
[0046]
A Reed-Solomon code is used as this error correction code. This Reed-Solomon code is a powerful error correction method capable of correcting an error in byte units, and can correct an error of half or less of the error correction code length. The details of Reed-Solomon error correction codes are described in many books such as Shogodo “Code Theory (Computer Fundamental Course 18)” by Miyagawa, Iwabuchi, and Imai.
[0047]
In this embodiment, since the error correction code length is 2 bytes, 1-byte error correction is possible.
[0048]
The code image creation unit 23 assigns the data and the error correction code data to each cell of the two-dimensional code as shown in FIG. 2, and creates a two-dimensional code image.
[0049]
Data and error correction code data are assigned to cells in which integer 56-bit data is 1 to 56 cells and error correction code data 16 bits is 57 to 72 cells. Note that the specified vertex coordinates and cell center coordinates are used when creating the code image. This specified coordinate is also used when reading the code.
[0050]
The code may be generated by software, and FIG. 7 shows a flowchart of the method.
[0051]
First, in step S101, a numeric character string is input. In step S102, the numeric character string is converted into integer type 56-bit data that is normally handled in the computer. The data converted to integer data is added with 16 bits of error correction code in step S103. As this error correction code, a Reed-Solomon code is used as before. The data and error correction code data created in this way are assigned to each cell of the two-dimensional code in step S104 as shown in FIGS. 2 and 4, and a two-dimensional code image is created. Similarly to the previous case, the data is assigned to cells in which the integer type 56-bit data is 1 to 56, and the error correction code data 16 bits is 57 to 72 cells. In addition, when creating a code image, the specified vertex coordinates and cell center coordinates are used. This specified coordinate is also used when reading the code.
[0052]
Next, a barcode reader that reads the above-described two-dimensional code will be described. As shown in FIG. 8, the barcode reader includes a vertex candidate detection unit 11, a code frame detection unit 12, a projective transformation coefficient calculation unit 13, a data sampling unit 14, an error correction unit 15, and a format conversion. Part 16.
[0053]
The vertex candidate detection unit 11 detects a vertex candidate of the two-dimensional code. The code frame detection unit 12 detects a code frame to be described later based on the vertex candidates. By detecting the code frame, the vertex candidate is determined as a true vertex.
[0054]
The projective transformation coefficient calculation unit 13 calculates each cell of the created code from the coordinates of each vertex of the code frame detected by the code frame detection unit 12 and the specified coordinates of each vertex of the code frame when the code image is created. A projective transformation coefficient that is a coefficient for mapping the prescribed center coordinates of the cell and the center of each cell of the read code image is obtained.
[0055]
The data sampling unit 14 samples the data of the two-dimensional code using the projection transformation coefficient obtained by the projection transformation coefficient calculation unit 13. The error correction unit 15 determines whether the data read by the data sampling unit 14 is an error. If there is no error or error correction is possible, the format conversion unit 16 converts the integer type 56-bit data other than the error correction code. Output to. The format converter 16 converts the data into a numeric character string and outputs it.
[0056]
Next, details of processing of the vertex candidate detection unit 11 will be described. First, as shown in FIG. 9, oblique scanning is performed from the four corners of the image to detect black pixels. Let the detected black pixels be A, B, C, and D.
[0057]
Next, as shown in FIG. 10, the vertex candidate detection unit 11 performs one side of the cell from A, B, C, and D in the direction of the arrow, for example, 45 degrees lower right for A and 45 degrees lower left for B. The pixels are tracked by the number of pixels which is 1 / √2 of the number of pixels in the above, and it is determined whether or not they are all black pixels. If all of them are black pixels, the vertex candidate detection unit 11 detects A, B, C, and D as vertex candidates, and moves the processing to the code frame detection unit 12.
[0058]
The code frame detection unit 12 will be described. As shown in FIG. 11, the code frame detection unit 12 passes through black frame determination lines 52, 53, 54, and 55 that connect the end points tracked by the number of pixels of 1 / √2 from the vertex candidates A, B, C, and D. If the ratio of black pixels of pixels is 80% or more for every straight line in each straight line, it is determined that it is a code frame, and vertex candidates A, B, C, and D are designated as vertices A, B, and C of the two-dimensional code. , D is confirmed. At this time, the coordinates of the vertices A, B, C, and D are also detected.
[0059]
Next, the projective transformation coefficient calculation unit 13 will be described. In the projective transformation coefficient calculation unit 13, each cell of the generated code is determined from the coordinates of each vertex of the code frame detected by the code frame detection unit 12 and the specified coordinates of each vertex of the code frame when the code image is generated. A projective transformation coefficient that is a coefficient for mapping the prescribed center coordinates of the cell and the center of each cell of the read code image is obtained. A method for obtaining the projective transformation coefficient will be described later.
[0060]
Next, the data sampling unit 14 will be described. The data sampling unit 14 receives the code image, the specified center coordinates of each cell when the code is created, and the projective transformation coefficient. The data sampling unit 14 performs projective transformation on the specified center coordinates of each cell of the generated code image using the projection transformation coefficient obtained by the projection transformation coefficient calculation unit 13, thereby the center coordinates of each cell of the read code image. Ask for.
[0061]
The data sampling unit 14 sets the obtained center coordinate as the sampling center coordinate of the image data, and sets it to “1” if the number of black pixels of 3 × 3 pixels centering on the coordinate exceeds the number of white pixels, and to “0” otherwise. Read data. The read 72-bit data is input and arranged in the error correction unit 15, and determination of error correction is performed. If there is no error or error correction is possible, the error correction unit 15 outputs integer type 56-bit data other than the error correction code to the format conversion unit 16. The format converter 16 converts the data into a numeric character string and outputs it, thereby restoring the numeric character string.
[0062]
The above-described reading of the two-dimensional code may be performed by software, and FIG. 12 shows a flowchart of the method. In step S201, a code image obtained by imaging a two-dimensional code is input from diagonally above. In step S202, a vertex candidate of a two-dimensional code is detected.
[0063]
In step S202, as shown in FIG. 9, diagonal scanning is performed from the four corners of the image, and black pixels are detected. Let the detected black pixels be A, B, C, and D. Further, in step S202, as shown in FIG. 10, the number of pixels on one side of the cell is 1 in the direction of arrows from A, B, C, D, for example, 45 degrees lower right for A and 45 degrees lower left for B. Pixels are tracked by the number of pixels of / √2, and it is determined whether they are all black pixels. If they are all black pixels, A, B, C and D are detected as vertex candidates, and the process proceeds to step S203.
[0064]
In step S203, as shown in FIG. 11, the ratios of the black pixels passing through the black frame determination lines 52, 53, 54, and 55 connecting the end points tracked from the vertex candidates A, B, C, and D are all If 80% or more exists in each straight line, it is determined that it is a code frame, and vertex candidates A, B, C, D are determined as vertices A, B, C, D of the two-dimensional code. At this time, the coordinates of the vertices A, B, C, and D are also detected.
[0065]
If no code can be detected in step S203, the process branches to the end in the branching process in step S204, and the reading ends. If a code is detected, the process proceeds to the next step S205.
[0066]
In step S205, the specified center coordinates of each cell of the generated code are read from the coordinates of each vertex of the code frame detected in step S203 and the specified coordinates of each vertex of the code frame when the code image is generated. A projective transformation coefficient that is a coefficient for mapping the center of each cell of the code image is obtained.
[0067]
In the next step S206, the code image, the projective transformation coefficient, and the specified center coordinates of each cell of the created code image are input, and the specified center of each cell of the created code image is obtained using the projective transformation coefficient obtained in step S205. By performing projective transformation of the coordinates, the center coordinates of each cell of the read code image are obtained.
[0068]
Further, in step S206, the obtained center coordinate is set as the sampling center coordinate of the image data. If the number of black pixels of 3 × 3 pixels centering on the coordinate exceeds the number of white pixels, it is set to “1”, and otherwise set to “0”. Data is read out. The read 72-bit data is subjected to error correction determination in step S207. If there is no error or error correction is possible, the integer type 56-bit data other than the error correction code is converted into a numeric character string in step S208, and the numeric character string is restored in step S209. .
[0069]
Next, how to obtain the projective transformation coefficient will be described. Projective transformation is transformation when a figure / object in a three-dimensional space is displayed on a two-dimensional plane / screen. This is a technique for converting the coordinates of an object in a three-dimensional space into coordinates on a two-dimensional plane, and is widely known as a three-dimensional image processing technique.
[0070]
In order to perform such projection conversion accurately, it is necessary to clarify the position of the imaging system, optical characteristics, and the like. However, considering the compatibility with various devices, it is difficult to specify and accurately determine the positions of the imaging systems of all the various devices.
[0071]
On the other hand, it is not always necessary to perform an accurate projective transformation in order to exhibit the effect of being easily read by an optical system that captures an image from an oblique direction. For example, a trapezoidal or various density code can be used to facilitate reading with an oblique optical system, rather than a conventional rectangular, two-dimensional code represented by uniform density cells. it can.
[0072]
A simple example for realizing such trapezoidal codes and various cell sizes will be described with reference to FIG. In the optical system as shown in FIG. 13, the read image is transformed into a trapezoidal figure by the projective transformation.
[0073]
At this time, the ratio (X1 / X3) between the long side and the short side of the trapezoid is equal to the reciprocal (L3 / L1) of the ratio of the distance from the image sensor to each area of the code. In this embodiment, instead of a two-dimensional code that is normally rectangular and has a uniform cell size, a two-dimensional code having a cell shape and size that can cancel the above projective transformation is created. .
[0074]
The cell size is determined at a constant magnification for each line of the 2D code. The magnification of this deformation is different in the distance from the imaging surface even in one line of the two-dimensional code. For example, the magnification at the middle position of one line is converted at the same magnification from the top to the bottom of one line. . By this operation, a two-dimensional code having a stepped cell boundary is obtained (two-dimensional code in FIGS. 1 and 3). Alternatively, a two-dimensional code as shown in FIG. 5 may be created by strictly performing projective transformation calculation.
[0075]
Next, strict projective transformation will be described with reference to FIG. FIG. 14 shows a code image 60 schematically showing a read code image and a code 61 schematically showing an electronically generated code. Further, As to Ds of the code image 60 and Ar to Dr of the code 61 are vertices, and xs1 and yr2 in parentheses represent coordinates. Psk and Prk represent the center coordinates of a certain cell.
[0076]
The vertices of As to Ds of the code image 60 and Ar to Dr of the code 61 satisfy the mathematical formula shown in FIG. These mathematical formulas (1) and (2) represent conversion formulas from code coordinates for determining the coordinates of the code, such as the code 61, to coordinates for determining the coordinates of the code image, such as the code image 60, from Ar to Dr to As. Define coordinate transformation to ~ Ds.
[0077]
Expression (1) is an expression related to the X coordinate of the vertex, Expression (2) is an expression related to the Y coordinate of the vertex, and the subscript i of Expression (1) and (2) moves from 1 to 4. In these mathematical formulas, b1 to b8 are conversion parameters, which are unknown numbers. These conversion parameters are obtained by substituting the coordinate values of Ar to Dr and As to Ds into an equation to generate and solve an eight-way linear simultaneous equation. When b1 to b8 are obtained, the center coordinate Prk of each cell of the electronically generated code can be converted to obtain the sampling center coordinate Psk of the code image.
[0078]
Next, a second embodiment for recognizing codes with high speed and high accuracy will be described. This embodiment is effective not only when a barcode reader is used but also when a reading device such as a scanner is used.
[0079]
Specifically, in the second embodiment, it is easy to determine whether or not a figure surrounded by a black frame is a code, or in which direction the code is oriented, and it is fast and accurate. Is.
[0080]
As the two-dimensional code used in the second embodiment, the code shown in FIG. 5 is used. As shown in FIG. 16, a predetermined pattern in which the direction of the code is uniquely determined at the center of the code (hereinafter referred to as a finder pattern). 62).
[0081]
As shown in FIG. 16, the finder pattern 62 includes 4 × 2 cells at the center of the code and is composed of 6 black cells and 2 white cells. The pattern has directionality.
By comparing this finder pattern 62 with a figure enclosed by a black rectangle, it is possible to quickly determine whether or not the figure enclosed by the black rectangle is a code, and the direction of the code is also immediately Can be determined. The reason is that if the finder pattern 62 is not provided, it cannot be determined whether or not the code is provided unless error correction is performed.
[0082]
Next, a two-dimensional code creation device will be described with reference to FIG.
[0083]
The two-dimensional code creation device includes a format conversion unit 70, an error correction unit 71, and a code image creation unit 72.
[0084]
The format conversion unit 20 converts the input numeric character string into integer type 48-bit data normally handled in the computer.
[0085]
The error correction unit 21 adds 16 bits of error correction code to the data converted into integer type 48-bit data and the 8-bit data that represents the finder pattern. A Reed-Solomon code is used as this error correction code.
[0086]
The code image creation unit 23 assigns the data and the error correction code data to each cell of the two-dimensional code as shown in FIG. 18, and creates a two-dimensional code image.
[0087]
As shown in FIG. 18, the data, the finder pattern, and the error correction code data are assigned to the cells. As shown in FIG. 18, the data representing the finder pattern is 1 to 8 and the integer type 48-bit data is 9 to 56 cells. Data 16 bits are 57 to 72 cells. When creating a code image, the specified vertex coordinates and cell center coordinates are used. This specified coordinate is also used when reading the code.
The code may be generated by software, and FIG. 19 shows a flowchart of the method.
[0088]
First, in step S301, a numeric character string is input. In step S302, the numeric character string is converted into integer type 48-bit data normally handled in the computer. In the next step S303, 8 bits of the finder pattern are input. Further, the data converted into the integer type data is added with 16 bits of the generated error correction code in step S304. As this error correction code, a Reed-Solomon code is used. The data, finder pattern, and error correction code data created in this way are assigned to each cell of the two-dimensional code in step S305, and a two-dimensional code image is created.
[0089]
As for the allocation of data to cells, the data representing the finder pattern is 1 to 8 in FIG. 18, the integer 48-bit data is 9 to 56 cells, and the error correction code data 16 bits is 57 to 72 cells. ing. Note that the specified vertex coordinates and cell center coordinates are used when creating the code image. This specified coordinate is also used when reading the code.
[0090]
Next, a barcode reader that reads the above-described two-dimensional code will be described. As shown in FIG. 20, the barcode reader includes a vertex candidate detector 81, a code frame detector 82, a projective transformation coefficient calculator 83, a finder detector 84, a data sampling unit 85, and an error correction. Section 86 and format conversion section 87.
[0091]
The vertex candidate detection unit 81 detects a vertex candidate of the two-dimensional code. The code frame detection unit 82 detects a code frame based on the vertex candidates. By detecting the code frame, the vertex candidate is determined as a true vertex.
[0092]
The projective transformation coefficient calculation unit 83 calculates each cell of the created code from the coordinates of each vertex of the code frame detected by the code frame detection unit 12 and the specified coordinates of each vertex of the code frame when the code image is created. A projective transformation coefficient that is a coefficient for mapping the prescribed center coordinates of the cell and the center of each cell of the read code image is obtained.
[0093]
The finder detection unit 84 performs processing related to the finder, such as finder detection. The data sampling unit 85 samples the data of the two-dimensional code using the projection conversion coefficient obtained by the projection conversion coefficient calculation unit 83. The error correction unit 86 determines whether or not the data read by the data sampling unit 85 is an error. If there is no error or error correction is possible, the format conversion unit 87 converts the integer type 56-bit data other than the error correction code. Output to. The format converter 87 converts the data into a numeric character string and outputs it.
[0094]
Next, details of the processing of the vertex candidate detection unit 81 will be described. Similarly to the vertex candidate detection unit 11 (see FIG. 8), the vertex candidate detection unit 81 performs oblique scanning from the four corners of the image as shown in FIG. 9 to detect black pixels. Let the detected black pixels be A, B, C, and D.
[0095]
Next, as shown in FIG. 10, the vertex candidate detection unit 81 performs a side of the cell such as A, B, C, D in the direction of the arrow, for example, 45 degrees lower right for A and 45 degrees lower left for B. The pixels are tracked by the number of pixels which is 1 / √2 of the number of pixels in the above, and it is determined whether they are all black pixels. If they are all black pixels, the vertex candidate detection unit 11 detects A, B, C, and D as vertex candidates, and moves the process to the code frame detection unit 82.
[0096]
The code frame detection unit 82 will be described. Similarly to the code frame detection unit 12, the code frame detection unit 82, as shown in FIG. 11, a black frame determination line 52 connecting the end points traced by the number of pixels 1 / √2 from the vertex candidates A, B, C, D. If the ratio of black pixels passing through 53, 54 and 55 is 80% or more for every straight line in all the straight lines, it is determined that it is a code frame, and vertex candidates A, B, C and D are two-dimensionally displayed. Determine as vertices A, B, C, D of the code. At this time, the coordinates of the vertices A, B, C, and D are also detected.
[0097]
Next, the projective transformation coefficient calculation unit 83 will be described. Similarly to the projection conversion coefficient calculation unit 13, the projective conversion coefficient calculation unit 83 also defines the coordinates of each vertex of the code frame detected by the code frame detection unit 82 and the vertices of the code frame when the code image is created. From the coordinates, a projective transformation coefficient that is a coefficient for mapping the specified center coordinates of each cell of the generated code and the center of each cell of the read code image is obtained.
[0098]
Next, the finder detection unit 84 will be described. The finder detection unit 84 attempts to detect a finder pattern. The finder detection unit 84 receives the code image, the projective transformation coefficient, and the specified center coordinates of each cell constituting the finder pattern of the created code.
[0099]
The finder detection unit 84 obtains the center coordinates of each cell of the read code image by performing projective transformation on the specified center coordinates of each cell constituting the finder pattern of the generated code image.
[0100]
The finder detection unit 84 sets the obtained center coordinate as the sampling center coordinate of the image data, and sets “1” if the number of black pixels of 3 × 3 pixels centered on the coordinate exceeds the number of white pixels, and sets it to “0” otherwise. The data is read, and the read data is compared with the true data of the finder pattern. If there is one or less error, it is determined that the currently handled graphic is a code. If there are two or more errors, there is a possibility that the direction of the code does not match, so the finder pattern is detected by rotating the code by 90 degrees. Even if the finder pattern is detected in the four directions, if the finder is not detected, it is determined that the currently processed image is not a code.
[0101]
Next, the data sampling unit 85 will be described. Similarly to the data sampling unit, the data sampling unit 85 receives the code image, the specified center coordinates of each cell at the time when the code is created, and the projective transformation coefficient. The data sampling unit 85 performs projective transformation of the specified center coordinates of each cell of the created code image using the projection transformation coefficient obtained by the projection transformation coefficient calculation unit 83, thereby the center coordinates of each cell of the read code image. Ask for.
[0102]
The data sampling unit 85 sets the obtained center coordinate as the sampling center coordinate of the image data, and sets “1” if the number of black pixels of 3 × 3 pixels centering on the coordinate exceeds the number of white pixels, and sets it to “0” otherwise. Read data. The read 72-bit data is input to the error correction unit 86 and arranged, and error correction is determined. If there is no error or if error correction is possible, data integer type 48-bit data other than the error correction code and finder pattern data is output to the format converter 87. The format converter 87 converts the data into a numeric character string and outputs it.
[0103]
The above-described two-dimensional code may be read by software, and FIG. 21 shows a flowchart of the method. In step S401, a code image obtained by imaging a two-dimensional code is input from diagonally above. In step S402, vertex candidates for the two-dimensional code are detected.
[0104]
In step S402, as shown in FIG. 9, diagonal scanning is performed from the four corners of the image, and black pixels are detected. Let the detected black pixels be A, B, C, and D. Further, in step S402, as shown in FIG. 10, the number of pixels on one side of the cell is 1 in the direction of arrows from A, B, C, D, for example, 45 degrees lower right for A and 45 degrees lower left for B. Pixels are tracked by the number of pixels of / √2, and it is determined whether they are all black pixels. If they are all black pixels, A, B, C, and D are detected as vertex candidates, and the process proceeds to step S403.
[0105]
In step S403, as shown in FIG. 11, the ratios of the black pixels that pass through the black frame determination lines 52, 53, 54, and 55 connecting the end points tracked from the vertex candidates A, B, C, and D are all If 80% or more of each straight line exists, it is determined that it is a code frame, and vertex candidates A, B, C, and D are determined as vertices A, B, C, and D of the two-dimensional code. At this time, the coordinates of the vertices A, B, C, and D are also detected.
[0106]
If no code can be detected in step S403, the process branches to the end in the branch process of step S404, and the reading ends. If a code is detected, the process proceeds to the next step S405.
[0107]
In step S405, the specified center coordinates of each cell of the generated code are read from the coordinates of each vertex of the code frame detected in step S403 and the specified coordinates of each vertex of the code frame when the code image is generated. A projective transformation coefficient that is a coefficient for mapping the center of each cell of the code image is obtained.
[0108]
In the next step S406, detection of a finder is attempted. In step S406, the code image, the projective transformation coefficient, and the specified center coordinates of each cell constituting the finder pattern of the created code are input. In step S406, the center coordinates of each cell of the read code image are obtained by projective transformation of the specified center coordinates of each cell constituting the finder pattern of the generated code image, and this is obtained as the sampling center coordinates of the image data. If the number of black pixels of 3x3 pixels centered on the coordinates exceeds the number of white pixels, the data is read as '1', otherwise it is read as '0', and the read data is compared with the true data of the finder pattern If the number of errors is 1 or less, it is determined that the currently handled graphic is a code. If there are two or more errors, the direction of the code may not match, so the finder pattern is detected by rotating the code by 90 degrees. Even if the finder pattern is detected in the four directions, if the finder is not detected, it is determined that the currently processed image is not a code.
[0109]
When the finder detection is successful, the process proceeds to the next step S408, and when it is unsuccessful, the process is terminated.
[0110]
In the next step S408, the code image, the projective transformation coefficient, and the specified center coordinates of each cell of the created code image are input, and the specified center of each cell of the created code image is obtained using the projective transformation coefficient obtained in step S405. By performing projective transformation of the coordinates, the center coordinates of each cell of the read code image are obtained.
[0111]
Further, in step S408, the obtained center coordinate is set as the sampling center coordinate of the image data, and is set to '1' if the number of black pixels of 3x3 pixels centering on the coordinate exceeds the number of white pixels, otherwise set to '0'. Data is read out. The read 72-bit data is subjected to error correction determination in step S409. If there is no error or error correction is possible, the integer type 48-bit data other than the error correction code is converted into a numeric character string in step S410, and the numeric character string is restored in step S411. .
[0112]
In the above example, the usable data is reduced from 56 bits to 48 bits, but it is possible to increase the number of cells in the code to hold more data. If the number of cells is 14 × 8, 112 bits (14 bytes) of data can be stored. For example, 72 bits can be assigned to data, 8 bits can be assigned to the finder, and 32 bits can be assigned to error correction. How many cells are needed depends on the application.
In this embodiment, since it is possible to determine whether the code is a code using a finder pattern and to determine the direction of the code at high speed, various devices such as a scanner and a digital camera can be used in addition to the handwriting input device of FIG. It is possible to read the code using it.
[0113]
【The invention's effect】
As described above, according to the present invention, it is possible to provide a high-speed and high-accuracy two-dimensional code barcode reader.
[Brief description of the drawings]
FIG. 1 is a diagram showing a two-dimensional code.
FIG. 2 is a diagram showing data arrangement positions.
FIG. 3 is a diagram showing a two-dimensional code.
FIG. 4 is a diagram illustrating data arrangement positions.
FIG. 5 is a diagram illustrating projective transformation.
FIG. 6 is a diagram illustrating each part of the two-dimensional code creation device.
FIG. 7 is a flowchart showing a two-dimensional code creation process.
FIG. 8 is a diagram showing each part constituting the barcode reading apparatus.
FIG. 9 is a diagram illustrating vertex pixel detection.
FIG. 10 is a diagram illustrating vertex candidate detection.
FIG. 11 is a diagram illustrating code frame detection.
FIG. 12 is a flowchart showing a two-dimensional code reading process.
FIG. 13 is a diagram illustrating a state of reading from an oblique direction.
FIG. 14 is a diagram illustrating a code image and a code.
FIG. 15 is a diagram illustrating mathematical formulas for obtaining conversion parameters.
FIG. 16 is a diagram illustrating a two-dimensional code indicating a finder pattern.
FIG. 17 is a diagram illustrating each part of the two-dimensional code creation device.
FIG. 18 is a diagram illustrating data arrangement positions.
FIG. 19 is a flowchart showing a two-dimensional code creation process.
FIG. 20 is a diagram showing each part constituting the barcode reading apparatus.
FIG. 21 is a flowchart showing a two-dimensional code reading process.
FIG. 22 is a diagram illustrating a handwriting input device.
[Explanation of symbols]
11, 81 ... vertex candidate detection unit
12, 82 ... code detector
13, 83 ... Projection conversion coefficient calculation unit
14, 85 ... Data sampling unit
15, 86 ... Error correction section
16, 20, 70, 87 ... format conversion unit
21, 71 ... error correction section
23, 72: Code image creation unit
50, 51 ... 2D code
52, 53, 54, 55 ... black frame determination line
60 ... Code image
61 ... Code
62 ... Finder pattern
84 ... Finder detection section

Claims (7)

By capturing a barcode having a rectangular frame, a projected image obtained by projecting the barcode is obtained, and a barcode reader that reads information represented by the barcode,
When the black pixel is detected by performing oblique scanning from the four corners of the image including the projected image, the predetermined number of pixels are tracked in the predetermined direction from the detected black pixel, and the tracked pixels are all black pixels Vertex candidate detection means for detecting the detected black pixel as a vertex candidate of a frame of a barcode image that is a barcode photographed in the projected image;
Based on the vertex candidates, code frame detection means for detecting a frame of the barcode image and determining the vertex of the barcode image;
A projection transformation coefficient calculating means for calculating a projection transformation coefficient based on the vertex of the barcode frame and the vertex of the barcode image frame;
And a data sampling unit that acquires information represented by the barcode from the barcode image using the projective transformation coefficient.   The code frame detection means, when the ratio of pixels of the barcode among the pixels on the line segment connecting the pixels determined for each vertex candidate based on each vertex candidate is a predetermined ratio or more, 2. The barcode reading apparatus according to claim 1, wherein it is determined that a frame of the barcode photographed in the code image is detected. The inside of the barcode frame includes a plurality of information areas representing information in one of two colors for the data sampling means to acquire information,
The data sampling unit acquires colors of a plurality of pixels in the information area, and acquires information represented by the information area according to a ratio occupied by one of the acquired colors. Item 3. A barcode reader according to item 1 or 2 . It said data sampling means, according to claim 3, wherein the position of said information regions of the bar code, and the position of the imaging information area to the projection image, and wherein the associating with the projective transform coefficients Bar code reader. Wherein provided on the bar code, bar code reading according to claim 3 or 4, further comprising a finder detection unit that detects a pattern information represented by the information area disposed at a predetermined position device. Information the data sampling unit has acquired, if having correctable errors, reading the bar code according to any one of claims 1 to 5, further comprising error correction means for correcting該誤Ri apparatus. It said data sampling means bar code reader according to any of the preceding Claims 1, further comprising a format converting unit for converting the numerical values acquired information.

Images