JP5729348B2 - Character recognition device and character recognition method - Google Patents

Description

  The present invention relates to a character recognition device and a character recognition method.

  Conventionally, Patent Document 1 describes a character recognition method for storing information on a model set that may actually cause misreading.

  In this prior art, when the two candidate models obtained by the matching process are Mi1 and Mi2 (regardless of which of Mi1 and Mi2 is the first and second), the table element Ai refers to Will be.

  Then, the degree of difference Di1, Di2 between the character image to be recognized and each candidate model (here, Mi1, Mi2) is re-evaluated by multiplying the corresponding re-evaluation coefficients ki1, ki2, and ki1 × Di1 ≦ ki2 × Di2 In this case, the character category of the model Mi1 is the final result, and when ki1 × Di1> ki2 × Di2, the character category of the model Mi2 is the final result.

JP-A-8-194778

  However, according to the above prior art, the degree of difference from the candidate model is merely re-evaluated by multiplying it with a coefficient, and character recognition is not substantially performed. Moreover, it is considered that it is difficult to determine in advance an evaluation coefficient that is not erroneously recognized.

  The present invention has been made in view of the above points, and an object thereof is to improve the recognition accuracy of characters.

In order to achieve the above object, according to the first aspect of the present invention, an image acquisition unit 11 that acquires an inspection image obtained by photographing the inspection object 1 on which characters are written;
A support vector machine 129 for identifying a character appearing in the character area based on the feature amount obtained from the character area of the inspection image;
And learning data generation means S120 for generating learning data of the support vector machine 129 using the luminance value of the first cell B1 obtained by subdividing the difference area A1 between the character portions of the plurality of model images as a feature amount. To do.

  According to this, since a difference between a plurality of characters can be made apparent, erroneous recognition of characters can be suppressed and recognition accuracy can be improved.

  In addition, the code | symbol in the bracket | parenthesis of each means described in this column and the claim shows the correspondence with the specific means as described in embodiment mentioned later.

It is a whole block diagram of the character recognition apparatus in 1st Embodiment. It is a block diagram which shows the process part of FIG. It is a conceptual diagram of a support vector machine. It is a flowchart which shows the learning process of a support vector machine. It is a graph which shows the example of the result of having performed pattern matching in the learning process of a support vector machine. It is a graph which shows the example of the similar group produced in the learning process of a support vector machine. It is a flowchart which shows the learning data generation process of a support vector machine. It is a figure which shows the example of the result of having performed image processing in the learning data generation process of a support vector machine. It is a graph which shows the example of the learning table produced in the learning data generation process of a support vector machine. It is a flowchart which shows the character recognition process of a character recognition apparatus. It is a figure which shows the example of the character area image used for the character recognition process of a character recognition apparatus.

  Hereinafter, an embodiment will be described. A character recognition device 10 shown in FIG. 1 is installed in a production line of a fuel pump that supplies fuel to an engine of a vehicle. Characters such as a model number stamped on the surface of a fuel pump component (in this example, uppercase letters) recognize.

  The character recognition device 10 first performs character recognition by pattern matching to select a candidate character, and if there is a similar character similar to the candidate character, the brightness value of the cell extracted from a different area from the similar character is supported by the support vector machine As the input feature quantity, probability (confidence) indicating the possibility of being a candidate character is obtained.

  The character recognition device 10 includes an imaging unit 11 and a processing unit 12. The imaging unit 11 is an image acquisition unit that acquires an inspection image by photographing the workpiece 1 as an inspection object, and transmits the acquired inspection image to the processing unit 12. The imaging unit 11 may have an illumination light source that illuminates the workpiece 1.

  The processing unit 12 recognizes characters stamped on the surface of the workpiece 1 based on the inspection image acquired by the imaging unit 11 and performs various controls. The processing unit 12 includes a personal computer and its peripheral devices.

  As shown in FIG. 2, the processing unit 12 includes a control unit 121, a communication unit 122, a storage unit 123, a pattern matching unit 124, an XOR operation unit 125, an AND operation unit 126, a cell extraction unit 127, and an average luminance value calculation unit 128. And a support vector machine 129 and the like.

  The control unit 121 includes a central processing unit (CPU) of a personal computer and its peripheral circuits, and operates according to a program read into the CPU, and controls each unit of the imaging unit 11 and the processing unit 12.

  The communication unit 122 is a communication interface that transmits and receives control signals, image data, and data signals between the processing unit 12 and devices such as the imaging unit 11, and includes an I / O port and its driver.

  The processing unit 12 receives the inspection image from the imaging unit 11 through the communication unit 122. The control signal generated by the control unit 121 is transmitted to the imaging unit 11 through the communication unit 122. The processing unit 12 outputs the recognized character information to an external device through the communication unit 122.

  The storage unit 123 includes a semiconductor memory such as a random access memory (RAM) and a read only memory (ROM), a storage medium such as a magnetic disk and an optical disk, an access device to the storage medium, and the like. A program for performing and various data are stored.

  Data stored in the storage unit 123 includes, for example, a template image corresponding to each character that may be engraved on the work 1 (hereinafter referred to as a recognition target character), learning data of the support vector machine 129, and the imaging unit 11. There are inspection images taken by.

  The pattern matching unit 124, the XOR operation unit 125, the AND operation unit 126, the cell extraction unit 127, the average luminance value calculation unit 128, and the support vector machine 129 are functional modules implemented by programs executed on the CPU, for example. These means 124 to 129 may be mounted as a dedicated processing board including an image processing processor separate from the CPU.

  The pattern matching unit 124 compares the input image with the template image read from the storage unit 123 and performs pattern matching to recognize characters included in the input image. Specifically, the pattern matching unit 124 obtains a score representing the degree of coincidence between the input image and each template image, and determines a template image that maximizes the score. In this example, the score is obtained by the normalized correlation coefficient shown in the following formula 1.

ここでＲはスコア（一致度）であり、Ｉは入力画像の輝度値であり、Ｔはテンプレート画像の輝度値であり、ｗは画像の幅であり、ｈは画像の高さである。

Here, R is a score (degree of coincidence), I is the luminance value of the input image, T is the luminance value of the template image, w is the width of the image, and h is the height of the image.

  When the character included in the input image and the character included in the template image completely match, the score R = 1, and when there is no correlation between the character included in the input image and the character included in the template image, the score R = 0.

  The XOR operation means 125 obtains an exclusive OR (XOR) of the two images. The AND operation means 126 calculates a logical product (AND) of the two images. The cell extracting unit 127 extracts a cell having a predetermined size from a predetermined region in the image. The average luminance value calculating unit 128 calculates the average luminance value of each cell extracted by the cell extracting unit 127. Details of the XOR operation means 125, the AND operation means 126, the cell extraction means 127, and the average luminance value calculation means 128 will be described later.

  The support vector machine 129 identifies characters appearing in the image using the average luminance value of each cell calculated by the average luminance value calculating unit 128 as a feature amount. FIG. 3 shows a conceptual diagram of the support vector machine 129.

  When the predetermined identification object belongs to any of a plurality of categories, the support vector machine 129 determines to which category the identification object belongs based on the feature amount obtained from the identification object. It is a discriminator.

  The boundary between categories is represented by a set of features having the shortest distance from feature amounts of learning data belonging to adjacent categories among feature amounts of learning data belonging to each category. A feature amount representing a boundary between categories is called a support vector.

  In the example of FIG. 3, each point indicated by a circle is a feature quantity belonging to the category C1, and among these, the feature quantities 201 to 203 are support vectors of the category C1. Further, each point indicated by a rhombus is a feature quantity belonging to the category C2, and among these, the feature quantities 204 to 206 are support vectors of the category C2.

  In the support vector machine 129, in order to improve the identification accuracy, the support vector is determined so that the distance (margin) between the support vector of the category C1 and the support vector of the category C2 is maximized.

  In the support vector machine 129, even when the boundary between categories is non-linear, the feature quantity belonging to each category is determined by mapping the feature quantity of the learning data to a higher dimension using a kernel function and determining the support vector. By making it possible to perform linear separation, it is possible to obtain good discrimination performance.

  In the present embodiment, the support vector machine 129 is trained in advance using the average luminance value of cells extracted from different regions of a plurality of recognition target characters that are similar to each other as the feature amount of the learning data. A method of generating learning data used for learning of the support vector machine 129 will be described later.

  When the support vector machine 129 receives the average luminance value of the cells extracted from the different regions of the plurality of images, the support vector machine 129 obtains the probability indicating the certainty that is a specific recognition target character by using the average luminance value as the input feature amount. .

  The processing unit 12 displays the character recognition result by the pattern matching unit 124 or the support vector machine 129 on a display and notifies the user, or outputs the result to other devices connected to be communicable via the communication unit 122. .

  Next, a learning method of the support vector machine 129 will be described. Learning of the support vector machine 129 is performed by the processing unit 12 executing the processing shown in the flowchart of FIG.

  First, in step S100, an input image used for learning is generated. Specifically, the size of the model image corresponding to the recognition target character is automatically adjusted. In this example, the model image is stored in the storage unit 123 in advance.

  In subsequent step S110, similar characters are grouped. Specifically, the pattern matching unit 124 performs pattern matching between the template images using the normalized correlation coefficient expressed by the above-described Equation 1. Then, characters whose pattern matching score R exceeds a threshold value (0.7 in this example) are grouped as similar characters.

  FIG. 5 shows an example of the result of pattern matching for the recognition target character “C”. In this case, since the scores of the recognition target characters “D”, “G”, “O”, and “Q” exceed 0.7, the recognition target character “C” and the similar characters “C”, “D”, “ Group by a combination of “G”, “O”, and “Q”.

  In this example, a similar group is created by grouping with a combination of two types of characters. Specifically, four similar groups “C, D”, “C, G”, “C, O”, and “C, Q” are created.

  Such grouping is also performed on recognition target characters other than “C”. In this example, since the learning target character is an uppercase alphabet, for example, a similar group as shown in FIG. 6 is created. The created similar group is written and registered in the storage unit 123.

  In subsequent step S120, learning data used for learning of the support vector machine 129 is generated. Therefore, step S120 constitutes learning data generation means. Details of step S120 are shown in FIG.

  First, in step S1210, each template image corresponding to each recognition target character of the similar group created in step S110 is read from the storage unit 123 and input.

  In the following step S1220, a character area difference area is extracted from each template image input in step S1210, and the extracted character area difference area is subdivided into a number of cells. In subsequent step S1230, learning data is generated for a large number of cells subdivided in step S1220.

  FIG. 8A shows an example in which steps S1220 and S1230 are executed for similar groups “C, D”, and FIG. 8B shows that steps S1220 and S1230 are executed for similar groups “C, O”. An example is shown.

  In step S1220, the XOR operation means 125 obtains an exclusive logical sum (XOR) for each character portion of each template image corresponding to each recognition target character in the similar group, whereby the character portion difference area A1 (in the figure). White area) is extracted, and the cell extraction means 127 extracts the first cell B1 from the different area A1, thereby subdividing the different area A1 into a number of first cells B1.

  In the example of FIG. 8A, a character area difference area A1 is extracted by obtaining an exclusive OR (XOR) for each character part of the template images “C” and “D”. In the example of b), the character part difference area A1 is extracted by obtaining an exclusive OR (XOR) for each character part of the template images “C” and “O”.

  In this example, a rectangular first cell B1 is extracted from the different area A1. As a method of extracting the first cell B1 from the different area A1, for example, the different area is subdivided into a number of first cells B1 by searching the different area with a rectangular pattern corresponding to the first cell B1. can do.

  In step S1230, the average luminance value calculation unit 128 calculates the average luminance value of each first cell B1, and uses the average luminance value as learning data. In this example, the average luminance value of the image (gray image) of each first cell B1 is normalized with black being 0 and white being 1 to obtain learning data.

  In subsequent step S1240, a different area of the background part (part other than the character) is extracted from each template image input in step S1210, and the extracted different area of the background part is subdivided into a number of cells. In subsequent step S1250, learning data is created for a large number of cells subdivided in step S1240.

  FIG. 8C shows an example in which steps S1240 and S1250 are executed for the similar group “C, D”, and FIG. 8D shows that steps S1240 and S1250 are executed for the similar group “C, O”. An example is shown.

  In step S1240, the XOR operation means 125 obtains an exclusive OR (XOR) for each background portion of each template image corresponding to each recognition target character in the similar group, thereby differentiating the background portion difference area A2 (in the figure). White area) is extracted, and the cell extracting means 127 extracts the second cell B2 from the different area A2, thereby subdividing the different area A2 into a plurality of second cells B2.

  In the example of FIG. 8C, the background region difference area A2 is extracted by obtaining an exclusive OR (XOR) for each background portion of the template images “C” and “D”. In the example of d), the background portion difference area A2 is extracted by obtaining an exclusive OR (XOR) for each background portion of the template images “C” and “O”.

  In this example, a rectangular second cell B2 is extracted from the different area A2 as in step S1220. In the example of FIG. 8D, since the size of the different area A2 is small, no second cell B2 is extracted.

  In step S1250, as in step S1230, the average luminance value calculation unit 128 calculates the average luminance value of each second cell B2, and uses the average luminance value as learning data.

  In subsequent step S1260, a common area of the character part is extracted from each template image input in step S1210, and the extracted common area of the character part is subdivided into a number of cells. In the subsequent step S1270, learning data is created for a large number of cells subdivided in step S1260.

  FIG. 8E shows an example in which steps S1260 and S1270 are executed for the similar group “C, D”, and FIG. 8F shows that steps S1260 and S1270 are executed for the similar group “C, O”. An example is shown.

  In step S1260, the AND operation means 126 obtains a logical product (AND) for each character portion of each template image corresponding to each recognition target character of the similar group, thereby obtaining a common area A3 (white color in the figure) of the character portion. Area), and the cell extraction means 127 extracts the third cell B3 from the common area A3, thereby subdividing the common area A3 into a number of third cells B3.

  In the example of FIG. 8E, the common area A3 of the character part is extracted by obtaining a logical product (AND) for each character part of the template images “C” and “D”, and FIG. In the example, the common area A3 of the character part is extracted by obtaining a logical product (AND) for each character part of the template images “C” and “O”.

  In this example, a rectangular third cell B3 is extracted from the common area A3, as in steps S1220 and S1240.

  In step S1270, as in steps S1230 and S1250, the average luminance value calculation unit 128 calculates the average luminance value of each third cell B3, and uses the average luminance value as learning data.

  In subsequent step S1280, the learning data created in steps S1230, S1250, and S1270, that is, the average luminance value data of the first, second, and third cells B1, B2, and B3 are merged to create an SVM learning table. FIG. 9 shows an example of a learning table created for a similar group of “C, O”.

  In step S1280, weighting is performed by adjusting the number of first, second, and third cells B1, B2, and B3. Specifically, the total number of the first and second cells B1 and B2 in the different areas A1 and A2 of the character part and the background part is larger than the number of the third cells B3 in the common area of the character part. The first, second, and third cells B1, B2, and B3 are thinned out as appropriate.

  More specifically, with respect to the total number of cells on the learning table (100 in the example of FIG. 9), the sum of the first and second cells B1 and B2 of the different areas A1 and A2 of the character part and the background part. The number is preferably 70% or more, and the number of the third cells B3 in the common area of the character part is preferably less than 30%.

  At this time, the first, second, and third cells B1, B2, B2, B3 are positioned as evenly as possible in the respective regions A1, A2, A3. It is preferable to thin out B3. In order to facilitate understanding of the thinning of the first, second, and third cells B1, B2, and B3, in FIG. 8E and FIG. 8F, the third cell B3 that has been thinned and reduced. Are schematically shown in the character area common area A3.

  In this example, as a model image as a learning target of the support vector machine 129, in addition to the same image as the template image corresponding to the recognition target character, the character is smeared or a part of the character is missing. A plurality of images are included, and learning data extracted from these unclear model images is also included in the default setting. In FIG. 9, the learning data extracted from the unclear model image is shown surrounded by a thick frame. The robustness of the support vector machine 129 can be improved by using learning data extracted from such a blurred model image.

  Although not shown in FIG. 9, in this example, position information (relative position with respect to the center of gravity) of each cell B1, B2, B3 is also included in the learning table.

  In step S1290, the learning data (learning table) merged in step S1280 is output and written to the storage means 123.

  In step S130, SVM learning (support vector machine learning) is performed. Specifically, the learning data (learning table) created in step S120 is inserted into the support vector machine 129. Thus, the learning process of the support vector machine 129 is completed.

  Next, a character recognition method using the character recognition device 10 will be described. Character recognition using the character recognition device 10 is performed by the processing unit 12 executing the processing shown in the flowchart of FIG. 10 after the learning processing of the support vector machine 129 is completed.

  First, in step S200, an inspection image photographed by the imaging unit 11 is input. In subsequent step S210, an image of an area in which characters are shown (hereinafter referred to as a character area image) is cut out from the inspection image input in step S200.

  In subsequent step S220, pattern matching is performed between the character region image cut out in step S210 and the template image corresponding to each recognition target character. Specifically, the pattern matching unit 124 uses the normalized correlation coefficient expressed by the above-described Equation 1 between the character region image cut out in step S210 and the template image corresponding to each recognition target character. Perform pattern matching.

  In the subsequent step S230, the similar group registered in the storage unit 123 is searched for the character having the highest score (hereinafter, the highest score character) obtained by the pattern matching in step S220.

  In step S240, based on the search result in step S230, it is determined whether there is a similar group registered. If it is determined that a similar group is registered, the process proceeds to step S250, and SVM determination (support vector machine determination) is performed.

  Specifically, processing similar to that in steps S1220 to S1270 described above is performed based on both the character region image cut out in step S210 and the template image of similar characters belonging to the similar group searched in step S230. Then, the difference area of the character part, the difference area of the background part, and the common area of the character part of both images are extracted, and a large number of cells are extracted from each extracted area to obtain the average luminance value of each cell. Then, by using the average luminance value of each cell as the input feature amount of the support vector machine, the probability is obtained for the highest score character in the pattern matching in step S220.

  On the other hand, if it is determined in step S240 that no similar group is registered, the process proceeds to step S260, and the highest score character in the pattern matching in step S220 is selected as the first candidate character.

  In step S270 following steps S250 and S260, it is determined whether or not the pattern matching score in the first candidate character selected in step S260 or the probability obtained in the SVM discrimination in step S240 is greater than or equal to a threshold value.

  If it is determined that the value is greater than or equal to the threshold value, the process proceeds to step S280, and the highest score character (first candidate character) in the pattern matching of step S220 is output as a recognized character to an output target device such as a display.

  On the other hand, when it determines with it being less than a threshold value, it progresses to step S290 and outputs that it was impossible to identify a character (recognition NG) to output object apparatuses, such as a display.

  Note that the threshold used in step S270 is appropriately set according to the recognition accuracy required for the character recognition device 10. In this example, the threshold is set to 0.7.

  Next, an example of the result of performing character recognition by the process shown in the flowchart of FIG. 10 will be described. FIGS. 11A and 11B show examples of character area images cut out in step S210.

  FIG. 11A shows an image in which the character “C” can be clearly seen. The result of executing character recognition on this image is as follows.

  In the pattern matching in step S220, the highest score character is “C”, and the score is 0.999.

  Therefore, in step S230, a similar group is searched for the character “C”. In step S240, “C, D”, “C, G”, “C, O”, “C, Q” are registered as the similar groups. It was determined that there were four.

  Accordingly, in step S250, SVM discrimination is performed for four similar groups “C, D”, “C, G”, “C, O”, “C, Q”, and as a result, the similarity of “C, D” is determined. For the group, the letter “C” has a probability of 0.949, for the similar group “C, G”, the letter “C” has a probability of 0.949, and for the similar group “C, O”, the letter “C”. The probability of the letter “C” is 0.948 for the similar group “C, Q”.

  Therefore, in the determination in step S270, it is determined that both the pattern matching score and the SVM discrimination probability exceed the threshold value 0.7, and the character “C” is output as a recognized character in step S280.

  On the other hand, FIG. 11B shows an image in which the character “C” is unclear, and the result of executing character recognition on this image is as follows.

  In the pattern matching in step S220, the highest score character is “C”, and the score is 0.33.

  Therefore, in step S230, a similar group is searched for the character “C”. In step S240, “C, D”, “C, G”, “C, O”, “C, Q” are registered as the similar groups. ”Was determined.

  Accordingly, in step S250, SVM discrimination is performed for four similar groups “C, D”, “C, G”, “C, O”, “C, Q”, and as a result, the similarity of “C, D” is determined. The probability of the letter “C” is 0.851 for the group, the probability of the letter “C” is 0.889 for the similar group “C, G”, and the letter “C” is for the similar group of “C, O”. The probability of the letter “C” is 0.900 for the similar group “C, Q”.

  Accordingly, in the determination in step S270, it was determined that the SVM discrimination probability exceeded the threshold value 0.7, and therefore the character “C” was output as a recognized character in step S280. Thus, a good recognition result could be obtained even for an image in which the letter “C” is unclear.

  According to the present embodiment, as described in steps S1220, S1230, and the like, the support vector machine 129 uses the luminance value of the first cell B1 obtained by subdividing the difference area A1 between character portions of a plurality of model images as a feature amount. Generate learning data.

  For this reason, compared with the case where the area of different area A1 is made into the feature-value, the difference between several characters can be made clear. For this reason, the recognition accuracy can be improved by suppressing erroneous recognition of characters.

  According to the present embodiment, as described in steps S1240, S1250, etc., learning data is generated using the luminance value of the second cell B2 obtained by subdividing the different area A2 between the background portions of the model image as a feature amount. For this reason, the difference between a plurality of characters can be further manifested, and thus erroneous recognition of characters can be further suppressed.

  According to the present embodiment, as described in steps S1260, S1270, etc., the learning data of the support vector machine 129 is generated using the luminance value of the third cell B3 obtained by subdividing the common area A3 between the character parts as the feature amount. . For this reason, the character recognition accuracy can be improved as compared with the case where the learning data of the support vector machine 129 is generated using only the luminance values of the cells B1 and B2 obtained by subdividing the different areas A1 and A2 as feature amounts.

  According to the present embodiment, as described in step S1280, the total number of first cells B1 and second cells B2 used for learning data is made larger than the number of third cells B3 used for learning data. For this reason, the detection margin by the support vector machine (129) can be expanded, and the character recognition accuracy can be further improved.

  According to the present embodiment, as described in step S240, the support vector machine 129 obtains the probability (confidence level) for the recognition target character having the highest score (matching degree) by pattern matching. That is, the recognition result by pattern matching is re-evaluated by the support vector machine 129. For this reason, the character recognition accuracy can be further improved.

(Other embodiments)
In the above-described embodiment, the learning data of the support vector machine 129 is generated using the average luminance value of the first, second, and third cells B1, B2, and B3 as the feature amount, but the first, second, and third The learning data of the support vector machine 129 may be generated using the maximum luminance values of the cells B1, B2, and B3 as feature amounts.

  In the above embodiment, a similar group is created by combining two types of characters and SVM discrimination is performed on two types of characters. However, a similar group is created by combining three or more types of characters. You may perform SVM discrimination | determination with respect to the character more than a kind.

  In the above embodiment, the total number of cells on the learning data of the support vector machine 129, the ratio of the numbers of the first, second, and third cells B1, B2, and B3 may be appropriately changed.

1 Workpiece (inspection object)
11 Imaging unit (image acquisition means)
124 Pattern matching means 129 Support vector machine A1 Character area difference area B1 1st cell A2 Background area difference area B2 2nd cell A3 Character area common area B3 3rd cell S120 Learning data generation means

Claims (7)

Image acquisition means (11) for acquiring an inspection image obtained by photographing the inspection object (1) on which characters are written;
A support vector machine (129) for identifying a character appearing in the character region based on a feature amount obtained from the character region of the inspection image;
Learning data generating means for generating learning data of the support vector machine (129) using the luminance value of the first cell (B1) obtained by subdividing the difference area (A1) between the character portions of the plurality of model images as a feature amount ( S120). A character recognition device comprising:   The learning data generating means (S120) generates the learning data using the luminance value of the second cell (B2) obtained by subdividing the different area (A2) between the background portions of the model image as a feature amount. The character recognition device according to claim 1.   The learning data generation means (S120) generates learning data of the support vector machine (129) using the luminance value of the third cell (B3) obtained by subdividing the common area (A3) between the character parts as a feature amount. The character recognition device according to claim 2, wherein:   The learning data generation means (S120) uses the total number of the first cell (B1) and the second cell (B2) used for the learning data of the third cell (B3) used for the learning data. The character recognition device according to claim 3, wherein the number is larger than the number.   The character recognition apparatus according to claim 1, wherein the plurality of model images are a set of images in which a degree of matching obtained by mutual pattern matching exceeds a threshold value. Pattern matching means (124) for performing pattern matching by comparing the character region with a template image corresponding to a recognition target character and obtaining a degree of matching with the recognition target character;
6. The character recognition device according to claim 1, wherein the support vector machine (129) obtains a certainty factor for the recognition target character having the highest degree of coincidence. Generating learning data of the support vector machine (129) using the luminance value of the first cell (B1) obtained by subdividing the difference area (A1) between the character portions of the plurality of model images as a feature amount (S120);
A step (S200) of acquiring an inspection image obtained by photographing the inspection object (1) on which characters are written;
Using the support vector machine (129) to identify a character appearing in the character area based on a feature amount obtained from the character area of the inspection image (S250). Method.

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