Information processing device and computer program

Abstract

To provide an information processor and a computer program that can perform a document assortment work efficiently and can improve the accuracy of the assortment.SOLUTION: An information processor comprises: an acquisition part that acquires a document image; an image generation part that performs subtractive color processing based on a plurality of threshold values for the acquired document image to generate a plurality of post-processing images; a text data generation part that optically reads out the plurality of generated post-processing images, respectively to generate text data; and a sorter that sorts the document images based on the plurality of generated text data.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing device and a computer program. [Background technology]

[0002] BPO (Business Process Outsourcing), which outsources operations that a company does not have the operational know-how to external companies, is being carried out in various fields. For example, there is an increasing demand for BPO services that outsource the reception, sorting, and review of documents related to various applications all at once.

[0003] Document sorting work requires the document to be scanned. Patent Document 1 discloses a process in which an operator inspects documents scanned by a scanner and electronically manages them, and in many cases, document sorting work involves manually classifying documents scanned by a scanner. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2006-94036 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, if human error occurs due to worker fatigue, the sorting work must be performed again, which not only reduces work efficiency but also reduces sorting accuracy.In addition, if a labor shortage occurs due to a sudden increase in cases, there is a risk that business opportunities will be missed because work cannot be outsourced in a timely manner.

[0006] The present invention has been made in view of the above circumstances, and has an object to provide an information processing device and a computer program that can make document sorting work more efficient and improve sorting accuracy. [Means for solving the problem]

[0007] The information processing device includes an acquisition unit that acquires a document image, an image generation unit that performs color reduction processing based on multiple thresholds on the document image acquired by the acquisition unit to generate multiple processed images, a text data generation unit that optically reads each of the multiple processed images generated by the image generation unit to generate text data, and a classifier that classifies the document image based on the multiple text data generated by the text data generation unit.

[0008] The computer program causes the computer to execute the following processes: acquire a document image, perform color reduction processing on the acquired document image based on multiple threshold values ​​to generate multiple processed images, optically read each of the multiple processed images to generate text data, and classify the document image based on the multiple text data generated. Effect of the Invention

[0009] According to the present invention, document sorting work can be performed efficiently and with improved sorting accuracy. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram illustrating an example of a configuration of an information processing system. [Diagram 2] FIG. 2 is a schematic diagram showing an example of a document image. [Diagram 3] 11 is a schematic diagram showing an example of a document image after color reduction processing; FIG. [Figure 4] FIG. 2 is a schematic diagram showing an example of text data. [Diagram 5] FIG. 2 is a schematic diagram showing an example of text data obtained by concatenating text-converted data; [Figure 6] FIG. 2 is a schematic diagram showing an example of character segmentation using character 2-grams. [Figure 7] FIG. 13 is a schematic diagram showing an example of the frequency of appearance of divided character strings. [Figure 8]FIG. 13 is a schematic diagram showing an example of a method for calculating weights. [Figure 9] FIG. 13 is a schematic diagram showing an example of character feature amounts to which weighting is applied. [Figure 10] FIG. 1 is a schematic diagram showing a first example of a document classification method using a trained model. [Figure 11] FIG. 11 is a schematic diagram showing a second example of a document classification method using a trained model. [Figure 12] FIG. 1 is a schematic diagram showing an example of a rule-based document classification method. [Figure 13] FIG. 13 is a schematic diagram showing an example of a method for identifying a contributing portion. [Figure 14] FIG. 13 is a schematic diagram showing an example of a highlighted document image. [Figure 15] 10 is a flowchart showing an example of a procedure for a document image classification process performed by an information processing apparatus. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Fig. 1 is a schematic diagram showing an example of the configuration of an information processing system. The information processing system includes an information processing device 50 and a terminal device 10. The information processing device 50 and the terminal device 10 are connected via a communication network 1. The terminal device 10 is configured by a personal computer, a tablet terminal, or the like, and is used by a worker.

[0012] The information processing device 50 includes a control unit 51 that controls the entire device, a communication unit 52, a storage unit 53, an image processing unit 54, an OCR processing unit 55, a feature extraction unit 56, a classifier 57, a weighting unit 58, a specification unit 59, and an output unit 60. The control unit 51 can be configured with a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), or the like.

[0013] The communication unit 52 has a function of communicating with the terminal device 10 via the communication network 1, and can transmit and receive required information. More specifically, the communication unit 52 has a function as an acquisition unit, and acquires a document image from the terminal device 10.

[0014] Fig. 2 is a schematic diagram showing an example of a document image. Fig. 2 shows an image of a driver's license as an example of a document image. The document includes documents for identity verification, and includes various documents such as a driver's license, a passport, a My Number card, insurance cards (health insurance cards, nursing care insurance cards, etc.), a national pension book, a disability certificate, a resident's card, a certified copy of a family register, a certified seal certificate, etc.

[0015] The storage unit 53 is composed of a semiconductor memory, a hard disk, or the like, and can store document images acquired via the communication unit 52. The storage unit 53 can also store required data such as processing results in the information processing device 50.

[0016] The image processing unit 54 has a function of performing image pre-processing on the document image acquired via the communication unit 52. The image pre-processing includes sharpening, color reduction, resizing, etc. In this specification, the image processing unit 54 is configured to include sharpening, color reduction, and resizing, but the sharpening, color reduction, and resizing may each be configured as a separate processing unit. Each image pre-processing will be described below.

[0017] The image processing unit 54 performs a sharpening process on the document image (or the image after a predetermined image processing is performed on the document image) acquired via the communication unit 52. The sharpening process emphasizes the contours using a filter that emphasizes the contrast of edges. The sharpening process can be performed on the image before or after the color reduction process.

[0018] FIG. 3 is a schematic diagram showing an example of a document image after color reduction processing. The image processing unit 54 has a function as an image generating unit, and performs color reduction processing based on a plurality of thresholds on the document image acquired via the communication unit 52 to generate a plurality of processed images. In the color reduction processing, pixel values ​​equal to or greater than the threshold are erased. In the example of FIG. 3, two document images after color reduction processing are generated based on two different thresholds Th1 and Th2. The pixel values ​​of R (red), G (green), and B (blue) are expressed as 0 (black) to 255 (white). For example, the threshold Th1 can be (R: 100, G: 100, B: 100), and the threshold Th2 can be (R: 200, G: 200, B: 200), but are not limited thereto. In addition, the thresholds are not limited to the same value for each of R, G, and B, and different sets of thresholds may be used for R, G, and B.

[0019] In the area where the background and the characters overlap, the visibility of the characters decreases depending on the combination of the background color and the character color. In order to improve the visibility of the character part, the background color is removed by the color reduction process, but the character part may also be removed depending on the threshold. Therefore, by performing the color reduction process using two different thresholds, even if the character is removed by the color reduction process based on one threshold, it is possible to pick up the character that was missed by the color reduction process based on the other threshold. In FIG. 3A, the threshold value Th1=(R:100, G:100, B:100) is set, so that pixels with R, G, and B pixel values ​​of 100 or more can be removed. In the example of FIG. 3A, an example in which the characters "driver's license" have been removed is shown. On the other hand, in FIG. 3B, the threshold value Th1=(R:200, G:200, B:200) is set, so that pixels with R, G, and B pixel values ​​of 200 or more can be removed. In the example of FIG. 3B, the characters "driver's license" that were removed in the example of FIG. 3A remain without being removed.

[0020] As described above, by performing color reduction processing on a document image based on a plurality of threshold values, it is possible to improve the character recognition accuracy of OCR (Optical Character Recognition) processing, which will be described later.

[0021] The image processing unit 54 performs a resizing process on the document image (or the image after a predetermined image processing has been performed on the document image) acquired via the communication unit 52. The size of the document image may vary depending on the type of document. By performing a resizing process, the image processing unit 54 can convert the image into an image of an optimal size for OCR processing. The resizing process can be performed on the document image after the sharpening process, but it may also be performed on each of the two post-color reduction images generated by the color reduction process. The resizing process can be performed on the image before or after the color reduction process.

[0022] The OCR processing unit 55 has a function as a text data generating unit, and generates text data by optically reading an image after image pre-processing by the image processing unit 54. More specifically, the OCR processing unit 55 generates text data by concatenating a plurality of pieces of text data obtained by optically reading each of a plurality of processed images generated by the image processing unit 54.

[0023] Fig. 4 is a schematic diagram showing an example of text data. Fig. 4A is a text conversion of the color-reduction processed image shown in Fig. 3A, in which the characters "Valid until O month O day" and "123456789000" are converted to text. Fig. 4B is a text conversion of the color-reduction processed image shown in Fig. 3B, in which the characters "Valid until O month O day", "Driver's license", and "123456789000" are converted to text.

[0024] Fig. 5 is a schematic diagram showing an example of text data in which the converted text data are linked. In the example of Fig. 5, the converted text data of Fig. 4A and Fig. 4B are linked. In the example of Fig. 5, Fig. 4B is arranged below Fig. 4A to link the text, but the present invention is not limited to this, and Fig. 4B may be arranged on the right, left, or above Fig. 4A to link the text. By linking the two text files, they are combined into one.

[0025] The feature extraction unit 56 extracts character features from the text data generated by the OCR processing unit 55. Specifically, the feature extraction unit 56 extracts character features based on the frequency of occurrence in the text data of each character string obtained by dividing the text data generated by the OCR processing unit 55. Character n-grams can be used to divide the text data. Character n-grams are a method for dividing a document into n consecutive characters. Below, character 2-grams will be described as an example of character n-grams.

[0026] Fig. 6 is a schematic diagram showing an example of character segmentation using character 2-gram. The text data shown in Fig. 5 is used as the document before segmentation. Character 2-gram segments the text data in Fig. 5 into pairs of two consecutive characters. In the example of Fig. 6, the data is segmented into pairs of two characters, such as "O month", "O day", {until}, ...

[0027] FIG. 7 is a schematic diagram showing an example of the frequency of occurrence of divided character strings. The character string "O month" appears twice in the text data, so its frequency is 2. The character string "O day" appears twice in the text data, so its frequency is 2. The same applies below. Furthermore, the character string "drive" appears once in the text data, so its frequency is 1. The character string "license" appears once in the text data, so its frequency is 1. The character string "certificate" appears once in the text data, so its frequency is 1.

[0028] The feature extraction unit 56 extracts a feature vector (also called a character feature) in which each divided character string is an element and the frequency of the character string is the element value. For example, if the number of divided character strings is 100, the feature vector will be a 100-dimensional vector.

[0029] The divided character strings can be weighted in advance according to their importance. The weighting method will be described below.

[0030] The weighting unit 58 assigns weights to the frequency of character strings. For example, the TF-IDF method can be used as a weighting method. TF-IDF stands for Term Frequency (TF) and Inverse Document Frequency (IDF). TF represents the frequency of occurrence of a character string in a document. Here, "in a document" refers to all text data collected to obtain the weighting. The more frequently a character string appears in the document data, the more important the character string is considered to be. IDF represents the inverse of the document frequency at which a character string appears. A character string that appears in many texts is considered to have a low importance in a document. The weighting unit 58 can calculate the weighting by multiplying TF and IDF.

[0031] FIG. 8 is a schematic diagram showing an example of a method for calculating weights. The weighting unit 58 calculates a weight for each character string using document data as learning data. The document data is text data generated from documents such as a plurality of driver's licenses, passports, health insurance cards, and resident's certificates. In the example of FIG. 8, the weighting unit 58 generates weighting information 581 using the TF-IDF method. The weighting information 581 is information in which a character string is associated with a weighting for the character string. In the example of FIG. 8, the weighting is set to 5.0 for the character strings "insurance" and "driving", and 4.5 for the character string "resident". The same applies to other character strings.

[0032] As described above, if a character appears frequently in the document data, it is likely to be an important feature and will be weighted more heavily, whereas if a character appears in many sentences, it is unlikely to be an important feature and will be weighted less, making it easier for classifier 57 to classify document images.

[0033] The feature extraction unit 56 can extract character features by weighting the frequency of each of the divided character strings.

[0034] FIG. 9 is a schematic diagram showing an example of a character feature to which weighting is applied. In FIG. 9, the relationship between the character string and the corresponding frequency is the same as the example in FIG. 7. As described above, the weighting unit 58 generates weighting information in advance. As shown in FIG. 9, the weighting of each of the character strings "O month" to "valid" is 0.5, the weighting of each of the character strings "12" to "00" is 0.2, the weighting of each of the character strings "driving" and "license" is 5.0, and the weighting of the character string "certificate" is 1.0. A feature vector as a character feature is obtained by multiplying the frequency of each character string by the weighting, and the values ​​of the elements x1 to x13 of the feature vector are calculated. For example, the element x1 corresponds to the character string "O month", and the value of the element x1 is 2×0.5=1.0. The element x11 corresponds to the character string "driving", and the value of the element x11 is 1×5.0=5.0. The same applies to the other elements.

[0035] The classifier 57 classifies the document image based on the text data generated by the OCR processing unit 55. More specifically, the classifier 57 can classify the document image based on the character feature extracted by the feature extraction unit 56. The classification of documents by the classifier 57 may use a trained model generated by machine learning, or may use rules that associate character feature amounts with document types. First, a case where a trained model is used will be described.

[0036] 10 is a schematic diagram showing a first example of a document classification method using a trained model. The classifier 57 has a neural network 571 as a trained model. The classifier 57 can be configured by combining hardware such as a CPU (e.g., a multi-processor having multiple processor cores), a GPU (Graphics Processing Units), a DSP (Digital Signal Processors), and an FPGA (Field-Programmable Gate Arrays).

[0037] When the character feature (feature vector) extracted by the feature extraction unit 56 is input, the neural network 571 outputs a probability (a value between 0 and 1) for each document type. Examples of document types include a driver's license, a health insurance card, a passport, a resident's card, and others. The document types are not limited to the example of FIG. 10. The classifier 57 classifies the document image based on the type with the largest value among the probabilities for each document type output by the neural network 571. For example, if the probabilities of a driver's license, a health insurance card, a passport, a resident's card, and others are 0.8, 0.4, 0.3, 0.1, and 0.1, respectively, the document image is classified as a driver's license. Other machine learning models may be used instead of the neural network 571.

[0038] FIG. 11 is a schematic diagram showing a second example of a document classification method using a trained model. The classifier 57 includes a first classifier 572, a second classifier 573, and an averaging unit 574. When the character feature (feature vector) extracted by the feature extraction unit 56 is input, the classifiers 572 and 573 each output a probability (a value between 0 and 1) for each document type. The classifier 572 outputs probabilities of a driver's license, health insurance card, passport, resident's card, and others from output terminals A1 to A5. The classifier 573 outputs probabilities of a driver's license, health insurance card, passport, resident's card, and others from output terminals B1 to B5. The averaging unit 574 averages the probabilities output from the output terminals A1 and B1 to output the probability of a driver's license. The same applies to the health insurance card, passport, resident's card, and others. Classifier 57 classifies the document images based on the type having the largest probability among the probabilities for each document type output by averaging unit 574.

[0039] For example, a linear SVM (support vector machine) can be used as the first classifier 572, and for example, a gradient boosting tree can be used as the second classifier 573. By performing ensemble learning and combining multiple machine learning models, the classification accuracy of document images can be improved. Note that the machine learning models to be combined are not limited to linear SVM and gradient boosting tree.

[0040] Next, the rule-based case will be described.

[0041] 12 is a schematic diagram showing an example of a rule-based document classification method. The classifier 57 has a rule-based classifier 575 and a rule DB 577. The classifier 575 has a similarity calculation unit 576. The rule DB 577 stores rules that associate feature vectors with document types in advance. For example, a driver's license is associated with a feature vector V1, and an insurance card is associated with a feature vector V2. The same applies to other document types.

[0042] When the feature vector Vx extracted by the feature amount extraction unit 56 is input, the classifier 575 uses the function of the similarity calculation unit 576 to calculate the similarity between the input feature vector Vx and each feature vector recorded in the rule DB 577, and outputs the type associated with the feature vector with the greatest similarity as the document type. The similarity can be calculated using, for example, cosine similarity.

[0043] In general document classification using image processing, the type of document is determined based on the design and format of the document, as in pattern matching, and it is difficult to classify non-standard documents (such as insurance cards and resident cards). As described above, the information processing device 50 performs optical character recognition using the OCR processing unit 55, so that even non-standard documents can be classified with high accuracy, regardless of the design and format of the document.

[0044] In addition, the accuracy of character recognition by OCR processing is not 100%, and erroneous recognition occurs. As described above, the information processing device 50 performs multiple pre-processing (color reduction processing) with different thresholds by the image processing unit 54, so that it is possible to reduce the loss of characters, and as a result, it is possible to improve the character recognition accuracy of the subsequent OCR processing.

[0045] Conventionally, document sorting work has often been performed manually by classifying documents scanned by a scanner, which is prone to human error. As described above, the information processing device 50 uses a classifier generated by machine learning or a rule-based classifier, which enables automation of document sorting work, thereby improving the efficiency of document sorting work and reducing sorting errors.

[0046] Since the information processing device 50 classifies document images, it is possible to automate the document sorting process. However, in the process of reviewing documents related to various applications after the sorting process, it is necessary to accurately determine review items such as whether the documents required for the application are complete and whether the documents are valid, and manual review is essential. Below, a method of providing information useful for document review will be described.

[0047] The identification unit 59 identifies a contributing portion of the text data that contributed to the classification of the document image by the classifier 57. The output unit 60 outputs the document image in which an area corresponding to the contributing portion is highlighted. For example, the output unit 60 can output the highlighted document image to the terminal device 10 via the communication unit 52. Note that the output unit 60 may output only the document type to the terminal device 10.

[0048] FIG. 13 is a schematic diagram showing an example of a method for identifying a contributing portion. The elements of a feature vector are character strings in text data, and the frequency of occurrence of the character strings (weighted frequency) is the value of the element. That is, since each element of the feature vector corresponds to a character string in the text data, the position or coordinates of the character string in the text data can be recorded. Since the element that contributes to the classification of a document image is considered to be a portion with a large weighted frequency value, if the position or coordinates of the character string in the text data are known, the contributing portion that contributed to the classification of the document image can be identified based on the position or coordinates. Then, if the contributing portion in the text data is known, the area corresponding to the contributing portion of the document image can also be known.

[0049] Fig. 14 is a schematic diagram showing an example of a highlighted document image. In the example of Fig. 14, the document is a health insurance card, and the area corresponding to the contributing portion that contributed to the classification is surrounded by a thick line. The words "health insurance" and "insured person certificate" are written in the area corresponding to the contributing portion. The highlighting may be in a display form surrounded by a thick line as in Fig. 14, or may be in a display form such as highlighting using a color or pattern, which allows the person in charge of reviewing to easily determine the type of document.

[0050] As described above, the explanatory reasons that contributed to the classification of a document can be shown by highlighting text on the document image, thereby enabling document reviewers to accurately and quickly determine review items.

[0051] 15 is a flowchart showing an example of a procedure for classifying document images by information processing device 50. For convenience, the following description will be given with control unit 51 acting as the subject of the process. Control unit 51 acquires a document image (S11), and performs a sharpening process on the acquired document image (S12). Control unit 51 also performs a size change process (S13) as necessary.

[0052] The control unit 51 performs color reduction processing based on multiple threshold values ​​(S14), performs OCR processing on the multiple processed images generated by the color reduction processing to convert them into text (S15), and concatenates the converted text data to generate text data (S16).

[0053] Control unit 51 extracts character features (feature vectors) from the text data (S17), and inputs the extracted character features to classifier 57 to classify the document image (S18). Control unit 51 outputs to terminal device 10 the document image in which areas corresponding to the contributing parts that contributed to the classification are highlighted (S19).

[0054] The control unit 51 determines whether there are other document images (S20), and if there are other document images (YES in S20), repeats the processing from step S11 onwards, and if there are no other document images (NO in S20), ends the processing.

[0055] The information processing device 50 can also be realized by using a computer equipped with, for example, a CPU (e.g., a multiprocessor having multiple processor cores), a GPU (Graphics Processing Units), a RAM, etc. The information processing device 50 can be realized on a computer by loading a computer program (which can be recorded on a recording medium) that defines the procedure of processing as shown in Fig. 15 into a RAM provided in the computer and executing the computer program with a CPU (processor).

[0056] The information processing device includes an acquisition unit that acquires a document image, an image generation unit that performs color reduction processing based on multiple thresholds on the document image acquired by the acquisition unit to generate multiple processed images, a text data generation unit that optically reads each of the multiple processed images generated by the image generation unit to generate text data, and a classifier that classifies the document image based on the multiple text data generated by the text data generation unit.

[0057] The information processing device includes a sharpening processing unit that performs a sharpening process on the image before or after the color reduction process.

[0058] The information processing device includes a resizing processing unit that performs a resizing process on the image before or after the color reduction process.

[0059] In the information processing device, the text data generating section generates text data by concatenating a plurality of pieces of text data obtained by optically reading each of the plurality of processed images generated by the image generating section.

[0060] In the information processing device, the classifier classifies the document image based on character features extracted from the text data generated by the text data generation unit, using rules that associate character features with document types, or using a trained model that outputs a document type when character features are input.

[0061] The information processing device includes a feature extraction unit that extracts the character feature based on the frequency of appearance in the text data of each of character strings obtained by dividing the text data generated by the text data generation unit.

[0062] In the information processing device, the feature extraction unit includes a character n-gram technique.

[0063] The information processing device includes a weighting unit that preliminarily weights the frequency of character strings, and the feature extraction unit extracts the character feature by weighting the frequency of each of the divided character strings.

[0064] In the information processing device, the weighting unit includes a TF-IDF method.

[0065] The information processing device includes an identifying unit that identifies a contributing portion of the text data that contributed to the classification of the document image, and an output unit that outputs the document image in which an area corresponding to the contributing portion is highlighted.

[0066] The computer program causes the computer to execute the following processes: acquire a document image, perform color reduction processing on the acquired document image based on multiple threshold values ​​to generate multiple processed images, optically read each of the multiple processed images to generate text data, and classify the document image based on the multiple text data generated. [Explanation of symbols]

[0067] 1. Communication Network 10 Terminal Equipment 50 Information processing device 51 Control section 52 Communications Department 53 Storage section 54 Image processing section 55 OCR processing section 56 Feature Extraction Unit 57, 572, 573, 575 classifier 574 Addition and averaging section 576 Similarity calculation unit 577 Rules DB 571 Neural Networks 58 Weighting section 581 Weighting Information 59 Specific section 60 Output section

Claims

1. An acquisition unit for acquiring a document image; an image generating unit that performs color reduction processing based on a plurality of threshold values ​​on the document image acquired by the acquiring unit to generate a plurality of processed images; a text data generating unit that optically reads each of the plurality of processed images generated by the image generating unit and generates text data; a classifier that classifies the document image based on the plurality of text data generated by the text data generating unit; Equipped with The text data generation unit generating text data by concatenating a plurality of text data obtained by optically reading each of the plurality of processed images generated by the image generating unit; Information processing device.

2. A sharpening processing unit that performs a sharpening process on the image before or after the color reduction process is provided. The information processing device according to claim 1 .

3. a resizing processing unit that performs a resizing process on the image before or after the color reduction process, 3. The information processing device according to claim 1 or 2.

4. The classifier may be classifying the document image based on the character features extracted from the text data generated by the text data generation unit, using a rule associating character features with document types, or using a trained model that outputs a document type when character features are input; The information processing device according to claim 1 .

5. a feature extraction unit that extracts the character feature based on a frequency of appearance in the text data of each character string obtained by dividing the text data generated by the text data generation unit, The information processing device according to claim 4.

6. The feature extraction unit includes a character n-gram method. The information processing device according to claim 5 .

7. A weighting unit that pre-weights the frequency of character strings, The feature extraction unit weighting the frequency of each of the divided character strings to extract the character feature amount; 7. The information processing device according to claim 5 or 6.

8. The weighting unit includes a TF-IDF method. The information processing device according to claim 7.

9. an identification unit that identifies a contribution portion of the text data that contributed to the classification of the document image; an output unit that outputs the document image in which an area corresponding to the contributing portion is highlighted; Equipped with The information processing device according to claim 1 .

10. On the computer, Get document images, performing color reduction processing based on a plurality of threshold values ​​on the acquired document image to generate a plurality of processed images; Each of the generated multiple processed images is optically read to generate text data; classifying the document image based on the generated plurality of text data; generating text data by concatenating a plurality of text data obtained by optically reading each of the generated plurality of processed images; A computer program that executes a process.

Images