Information processing device and information processing method

Abstract

In the present invention, an information processing device is realized with which it is possible to accurately identify a character on the face of a document. An acquisition unit (11) acquires an image of the document. A cutout unit (12) cuts out one or multiple target characters from the image, including an image of the surroundings of the one or multiple target characters. An identification unit (13) inputs the image cut out by the cutout unit (12) to a learned model, thereby executing an identification process relating to the one or multiple target characters.

Description

[Technical field]

[0001] The present invention relates to an information processing device that recognizes target characters on a card. [Background technology]

[0002] Conventionally, as a method for determining whether an identification document such as a driver's license or a My Number card is a forgery, a method for checking the contents written on the document using a technology such as human eyes or OCR (Optical Character Recognition) is known. As a technology related to this, there are the inventions disclosed in the following Patent Documents 1 and 2.

[0003] Patent Document 1 relates to an object verification device, an object verification program, and an object verification method for verifying the authenticity of an object, and the object verification device is an object verification device for verifying the authenticity of an object, and includes an image acquisition unit for acquiring an image of the object, a verification area identification unit for identifying a verification area in the object where the authenticity should be verified, a described portion detection unit for detecting a plurality of described portions in the verification area in the image, and a verification unit for verifying the authenticity of the object by comparing the described portions in the image with each other.

[0004] Patent Document 2 relates to a recognition processing device and a program. The recognition processing device includes a means for recognizing features indicative of a classification of a document among documents of the same type from an image of the document, and a recognition means for applying a recognition process corresponding to the classification indicated by the recognized features to recognition target elements in the image, thereby recognizing the recognition target elements. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2013-188935 [Patent Document 2] Japanese Patent Application Publication No. 2020-042466 Summary of the Invention [Problem to be solved by the invention]

[0006] The inventions described in Patent Documents 1 and 2 have a problem in terms of accurately identifying characters printed on the face of the card.

[0007] One aspect of the present invention has been made in consideration of the above-mentioned problems, and one example of the object of the present invention is to provide a technology that can accurately identify characters printed on a card face. [Means for solving the problem]

[0008] An information processing device according to one embodiment of the present invention comprises an acquisition means for acquiring an image of a card face, an extraction means for extracting one or more target characters from the image, including images surrounding the one or more target characters, and an identification means for performing an identification process on the one or more target characters by inputting the image extracted by the extraction means into a trained model.

[0009] An information processing device according to one embodiment of the present invention comprises a training data acquisition means for acquiring training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with images surrounding the one or more target characters from an image of a card face, and a correct label for the one or more target characters, and a learning means for training a model using the training data, which receives as input an image obtained by cutting out one or more target characters together with images surrounding the one or more target characters from an image of a card face, and outputs a recognition result for the one or more target characters.

[0010] An information processing method according to one embodiment of the present invention involves acquiring an image of a card face, extracting one or more target characters from the image, including images surrounding the one or more target characters, and inputting the extracted images into a trained model, thereby performing a recognition process on the one or more target characters.

[0011] An information processing method according to one embodiment of the present invention obtains training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with images surrounding the one or more target characters from an image of a card face, and a correct label for the one or more target characters; the image obtained by cutting out one or more target characters together with images surrounding the one or more target characters from the image of the card face is used as input; and a model is trained using the training data to output a recognition result for the one or more target characters.

[0012] A method for manufacturing a trained model according to one embodiment of the present invention includes obtaining training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with images surrounding the one or more target characters from an image of a card face, and a correct label for the one or more target characters; inputting the image obtained by cutting out one or more target characters together with images surrounding the one or more target characters from the image of the card face, and using the training data to train a model that outputs a recognition result for the one or more target characters.

[0013] A computer program according to one embodiment of the present invention is a computer program for causing a computer to execute an information processing method, which involves acquiring an image of a card face, extracting one or more target characters from the image, including images surrounding the one or more target characters, and inputting the extracted images into a trained model, thereby performing a recognition process for the one or more target characters.

[0014] A computer program according to one embodiment of the present invention is a computer program for causing a computer to execute an information processing method, the information processing method comprising the steps of: acquiring training data including a plurality of pairs of images obtained by cutting out one or more target characters together with images surrounding the one or more target characters from an image of a card face, and correct labels for the one or more target characters; inputting the image obtained by cutting out one or more target characters together with images surrounding the one or more target characters from the image of the card face, and using the training data to train a model that outputs recognition results for the one or more target characters. Effect of the Invention

[0015] According to one aspect of the present invention, characters printed on the face of a card can be accurately identified. [Brief description of the drawings]

[0016] [Figure 1] 1 is a block diagram showing a functional configuration of an information processing device according to a first exemplary embodiment of the present invention. [Diagram 2] 1 is a flow chart showing the flow of an information processing method according to an exemplary embodiment 1 of the present invention. [Diagram 3] FIG. 11 is a block diagram showing a functional configuration of an information processing device according to a second exemplary embodiment of the present invention. [Figure 4] 11 is a flow chart showing the flow of an information processing method according to an exemplary embodiment 2 of the present invention. [Diagram 5] FIG. 11 is a block diagram showing a functional configuration of an information processing device according to an exemplary embodiment 3 of the present invention. [Figure 6] FIG. 2 is a diagram showing an example of a face of a card. [Figure 7] FIG. 11 is a flow chart showing the flow of an information processing method according to an exemplary embodiment 3 of the present invention. [Figure 8] FIG. 11 is a block diagram showing a functional configuration of an information processing system according to an exemplary embodiment 4 of the present invention. [Figure 9] 11 is a flowchart for explaining a processing procedure of a reading device according to an exemplary embodiment 4 of the present invention. [Figure 10] 11 is a flowchart illustrating a processing procedure of a server according to an exemplary embodiment 4 of the present invention. [Figure 11] FIG. 1 illustrates an example of the configuration of a computer. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Example 1 A first exemplary embodiment of the present invention will be described in detail with reference to the drawings. This exemplary embodiment is a basic form of the exemplary embodiments described later.

[0018] (Configuration of information processing device) The configuration of an information processing device 10 according to this exemplary embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the functional configuration of the information processing device 10. The information processing device 10 includes an acquisition unit 11, a cutout unit 12, and a recognition unit 13.

[0019] The acquisition unit 11 acquires an image of the face of the card. For example, the acquisition unit 11 acquires an image of the face of the card captured by a camera, an image of the face of the card read by a scanner, etc. In this exemplary embodiment, specific examples of the face of the card include, but are not limited to, a driver's license, a personal identification card, a passport, a health insurance card, etc. In this exemplary embodiment, the face of the card refers to an object having a predetermined format and including at least one character in the format in a broad sense. Examples of the material of the face on which the characters are printed or arranged include, but are not limited to, paper, resin, or a combination thereof.

[0020] The size of the face of the card may be larger than a card, such as A4 size, and is not limited to the card size described above.

[0021] The cut-out unit 12 cuts out one or more target characters, including a peripheral image of the one or more target characters, from the image of the face of the card acquired by the acquisition unit 11. As described later, the peripheral image is a ruled line around the one or more target characters, at least one other character, etc.

[0022] Here, in this embodiment, the ruled line refers to a general linear object existing around the target character. More specifically, in this embodiment, the ruled line is a concept including any of the lines on the top, bottom, left, and right of a rectangular frame such as the "Name" field or the "Type" field of a driver's license, or multiple lines, and is not limited to rectangular frames such as the "Name" field or the "Type" field of a driver's license, but also includes cases where there is a line only above the target character, or a case where there is a line only below the target character, etc.

[0023] The identification unit 13 executes identification processing for one or more target characters by inputting the image cut out by the cut-out unit 12 to the trained model. As described later, the trained model is, for example, a model generated by making a neural network (hereinafter, abbreviated as NN) perform deep learning. Here, examples of the neural network include a CNN (Convolutional Neural Network) and an RNN (Recurrent Neural Network). The model is not limited to these configurations, and may be other machine learning such as a SVM (Support Vector Machine), or may be a combination of these other machine learning and a neural network. In this exemplary embodiment, the term "model" is often used simply, but the model may also be expressed as an inference model, an estimation model, an identification model, or the like.

[0024] (Effects of the information processing device 10) As described above, in the information processing device 10 according to this exemplary embodiment, the cut-out unit 12 cuts out one or more target characters from the image of the face of the card, including the image surrounding the one or more target characters, thereby enabling the characters to be identified more accurately than when performing the recognition process on only the target characters.

[0025] (Flow of information processing method) The flow of the information processing method according to this exemplary embodiment will be described with reference to Fig. 2. Fig. 2 is a flow diagram showing the flow of the information processing method. First, the acquisition unit 11 acquires an image of the face of a card (S1).

[0026] Next, the cutout unit 12 cuts out one or more target characters including images surrounding the one or more target characters from the image acquired by the acquisition unit 11 (S2). Then, the recognition unit 13 inputs the image cut out by the cutout unit 12 to the trained model, thereby executing a recognition process for the one or more target characters (S3).

[0027] (Effects of information processing methods) As described above, in the information processing method according to this exemplary embodiment, one or more target characters are extracted from the image of the face of the card, including the image surrounding the one or more target characters, thereby enabling the characters to be identified more accurately than if identification processing were performed on only the target characters.

[0028] Exemplary embodiment 2 (Configuration of information processing device) 3 is a block diagram showing a functional configuration of an information processing device 20 according to exemplary embodiment 2. The information processing device 20 includes a teacher data acquisition unit 21, a teacher data storage unit 22, and a learning unit 23. In this exemplary embodiment, a case will be described in which the model to be learned includes a NN.

[0029] The teacher data acquisition unit 21 acquires teacher data from the teacher data storage unit 22, the teacher data including multiple pairs of images obtained by cutting out one or more target characters together with images surrounding the one or more target characters from an image of the face of a card, and correct labels (correct data) for the one or more target characters.

[0030] The teacher data storage unit 22 stores a plurality of teacher data created in advance. Here, the teacher data refers to, for example, a set of one or more target characters and their surrounding images cut out from an image of a genuine card face, and a correct label (correct data) for the one or more target characters.

[0031] The model is trained to learn the correspondence between one or more target characters and surrounding images cut out from an image of a genuine card face, and the correct label. For example, if there are 1000 types of target characters to be identified, 1000 output nodes are prepared, and the model is trained to output "1" (a value close to "1") to the output node corresponding to the target character, and to output "0" (a value close to "0") to the other output nodes. By training the model in this way, when one or more target characters to be identified and surrounding images are input to the model, the highest value is output to the target output node.

[0032] Moreover, the model outputs accuracy information indicating the accuracy of the classification process. As an example, the model outputs accuracy information corresponding to the value of each output node. As an example, the model outputs the value of the target output node as the accuracy information.

[0033] The learning unit 23 trains the model using the teacher data acquired by the teacher data acquisition unit 21. As described above, when using a NN, one or more target characters cut out from the image of the face of the card and their surrounding images are input to the input layer. Then, the model is trained using the so-called BP (Back Propagation) method, which back-propagates the difference (error) between the value output to the output node and the value of the corresponding correct label, and changes the weighting so as to reduce the error.

[0034] As described above, in the information processing device 20 according to this exemplary embodiment, the learning unit 23 trains a model using a pair of an image obtained by cutting out one or more target characters together with images surrounding the one or more target characters from an image of the face of a card, and a correct label (correct data) for the one or more target characters, making it possible to create a model that can accurately identify characters.

[0035] (Flow of information processing method) The flow of the information processing method according to this exemplary embodiment will be described with reference to Fig. 4. Fig. 4 is a flow diagram showing the flow of the information processing method. The information processing method shown in Fig. 4 can be regarded as a model learning method using the information processing device 20, or as a model manufacturing method using the information processing device 20.

[0036] First, the training data acquisition unit 21 acquires training data including multiple pairs of images obtained by cutting out one or more target characters together with images surrounding the one or more target characters from an image of the face of a card, and correct labels for the one or more target characters (S11).

[0037] Next, the learning unit 23 trains the model using the teacher data acquired by the teacher data acquisition unit 21 (S12). Then, it is determined whether the learning of the model is completed (S13). For example, when the model is trained using all the teacher data stored in the teacher data storage unit 22, it is determined that the learning of the model is completed.

[0038] If the learning of the model is not completed (S13, No), the process returns to step S11 and repeats the subsequent processes. If the learning of the model is completed (S13, Yes), the learning unit 23 outputs the learned model (S14).

[0039] As described above, in the information processing method according to this exemplary embodiment, a model is trained using a pair of an image obtained by cutting out one or more target characters together with images surrounding the one or more target characters from an image of a card face, and a correct label for the one or more target characters, thereby making it possible to create a model that can accurately identify characters.

[0040] Exemplary embodiment 3 The third exemplary embodiment of the present invention will be described in detail with reference to the drawings. Note that components having the same functions as those described in the first exemplary embodiment are denoted by the same reference numerals, and the description thereof will not be repeated.

[0041] (Example of configuration of information processing device) 5 is a block diagram showing a functional configuration of an information processing device 10a according to a third exemplary embodiment of the present invention. The information processing device 10a includes an acquisition unit 11, a cutout unit 12, a recognition unit 13a, and a discrimination unit 14.

[0042] FIG. 6 is a diagram showing an example of a card face. A driver's license will be described as an example of a card face, but the card face may be an identification document such as a My Number card, a passport, or a health insurance card, and is not limited to these. In this exemplary embodiment, the card face refers to an object having a predetermined format and including at least one character in the format in a broad sense. Examples of the material of the surface on which the characters are printed or arranged include paper, resin, or a combination thereof, but are not limited to these.

[0043] 6, a driver's license has a name column, a birth date column, an address column, a date of issue column, a type column, etc., and the positions of these columns are predetermined. Therefore, the cropping unit 12 can identify the columns of each item from the positional relationship within the entire image acquired by the acquisition unit 11.

[0044] For example, when extracting a name, the cutout unit 12 identifies the name field based on the coordinates in the entire image of the driver's license and extracts images of all character strings written in the name field. The method of extracting each character from the extracted character string image is not limited to this embodiment, but as an example, a conventionally known character extraction algorithm can be used.

[0045] When cutting out extracted characters, the cutting unit 12 cuts out the image including the surrounding image of one or more target characters. In the case of the name field shown in FIG. 6, the cutting unit 12 cuts out the image including the ruled lines around one or more target characters included in the name. Specifically, the cutting unit 12 cuts out at least one of the ruled lines arranged above the one or more target characters and the ruled lines arranged below the one or more target characters. Note that the image may be cut out including the ruled lines to the left and right of one or more target characters included in the name.

[0046] In addition, the "△" marks in the name column in FIG. 6 are merely abstract representations of kanji, hiragana, katakana, alphabet, etc., that represent personal names, and are not intended to limit the present exemplary embodiment.

[0047] Furthermore, when cutting out an extracted character, the cutting unit 12 may cut out at least one character other than the one or more target characters. For example, since the combination of two characters "Showa" and "Nissei" in the date of birth field is fixed, the cutting unit 12 cuts out the character "Wa" as well when cutting out the character "Showa", as shown in Fig. 6. Similarly, when cutting out the character "Ni", the cutting unit 12 cuts out the character "Nise" as well, as shown in Fig. 6.

[0048] The recognition unit 13a includes a name model 131, an address model 132, and a type model 133. The name model 131 is a model created using an information processing device according to the second exemplary embodiment shown in Fig. 3, and is a model that has been trained using training data created based on characters written in a name field.

[0049] The address model 132 is a model created using an information processing device according to the second exemplary embodiment shown in FIG. 3, and is a model that has been trained using training data created based on characters written in the address field.

[0050] Similarly, the type model 133 is a model created using an information processing device according to the second exemplary embodiment shown in FIG. 3, and is a model that has been trained using training data created based on the characters entered in the type column.

[0051] In FIG. 5, the identification unit 13a shows only three types of models: a name model 131, an address model 132, and a type model 133, but may also include models that have been trained on images of other items.

[0052] The recognition unit 13a identifies which item the image cut out by the cutout unit 12 corresponds to, and selects a model corresponding to the identified item from among a plurality of models (name model 131, address model 132, type model 133).The recognition unit 13a then uses the selected model to perform a recognition process on one or more characters included in the identified item.

[0053] The discrimination unit 14 determines whether the face of the note is genuine by referring to the results of the discrimination process. The results of the discrimination process also include one or more discriminated characters, and the discrimination unit 14 determines whether the face of the note is genuine by determining the format of the one or more discriminated characters. The discrimination unit 14 determines that the face of the note is genuine if the format satisfies predetermined requirements. As an example, the genuineness of the face of the note is determined by checking the format of the character strings for each item shown below.

[0054] (1) If there is a "[" in the name field, is there a "]" after it? (2) Whether the dates in the date of birth column, date of issue column, etc. are valid dates (3) Whether the issue number in the issue date column is a five-digit number The result of the identification process also includes one or more identified characters, and the discrimination unit 14 determines the authenticity of the bill by judging the consistency of the one or more identified characters. The discrimination unit 14 determines that the bill is authentic when, as the consistency, a description on the bill is not inconsistent with another description (satisfies a predetermined condition). As an example, the discrimination unit 14 may be configured to determine the authenticity of the bill by checking the consistency of character strings for each of the items listed below.

[0055] (1) If the maiden name is written on the document, is the date of issue after the specified date? (2) If the date is written in the Gregorian calendar, is the date of issue on or after the specified date? (3) Whether the expiration date is the month following the date of birth The result of the identification process may include one or more identified characters, and the discrimination unit 14 may be configured to discriminate the authenticity of the ticket by comparing the one or more identified characters with a predetermined character group. For example, the type column lists types such as "large", "small", "medium", and "moped", and these types are predetermined. Therefore, the discrimination unit 14 can compare the characters listed in the type column after identification by the identification unit 13a with these character groups (character groups including characters such as "large", "small", "medium", and "moped") and discriminate the authenticity of the ticket based on whether they match.

[0056] Furthermore, the discrimination unit 14 discriminates the authenticity of the face of the note by referring to the accuracy information. As described above, a model is created so that accuracy information is output when an image cut out by the cut-out unit 12 is input to a trained model, and the recognition unit 13a can output the accuracy information. If the accuracy information output from the recognition unit 13a is lower than a predetermined value, the discrimination unit 14 determines that the one or more identified characters are forged. This utilizes the fact that misrecognition increases when characters on the face of a note are created using different types of fonts. The discrimination unit 14 judges the authenticity of all one or more identified characters that have been subjected to the recognition process by the recognition unit 13a, and determines that the face of the note is forged if the accuracy information is determined to be lower than a predetermined value.

[0057] 7 is a flow diagram showing the flow of an information processing method according to the third exemplary embodiment of the present invention. First, the acquisition unit 11 acquires an image of the face of a card (S21). Then, the cut-out unit 12 cuts out one or more target characters, including images surrounding the one or more target characters, from the image of the face of the card acquired by the acquisition unit 11 (S22).

[0058] Next, the recognition unit 13a identifies an item included in the face of the card based on its position in the image of the face of the card (S23), and selects a model corresponding to the identified item (S24).The recognition unit 13a then uses the selected model to perform a recognition process on one or more characters included in the identified item (S25).

[0059] Finally, the discrimination unit 14 judges whether the face of the note is genuine or counterfeit by referring to the result of the recognition process by the recognition unit 13a (S26).

[0060] As described above, in the information processing device according to this exemplary embodiment, the cutout unit 12 cuts out an image including the ruled lines around one or more target characters. If the face of a banknote is forged, the distance between the target character and the ruled lines may vary slightly, or the target character may be slightly tilted relative to the ruled lines. Therefore, the recognition unit 13a inputs the image cut out by the cutout unit 12 to the trained model and executes recognition processing on one or more target characters, thereby making it possible to recognize the forgery of the face of the banknote.

[0061] Furthermore, the cutout unit 12 cuts out at least one character other than the one or more target characters. If the face of the banknote is forged, the distance between the target character and other characters may be slightly different, or the target character may be slightly tilted relative to the other characters. Therefore, the recognition unit 13a inputs the image cut out by the cutout unit 12 to the trained model and executes recognition processing on one or more target characters, thereby making it possible to recognize the forgery of the face of the banknote.

[0062] Furthermore, the recognition unit 13a identifies which item the image cut out by the cutout unit 12 corresponds to, and selects a model corresponding to the identified item from among a number of models (name model 131, address model 132, type model 133). Therefore, it is possible to have the model learn different types of fonts depending on the item, making it possible to more accurately identify counterfeit bills.

[0063] Furthermore, the discrimination unit 14 discriminates the authenticity of the face of the note based on the character strings of each item after the recognition unit 13a has recognized it. Therefore, it becomes possible to discriminate the authenticity of the face of the note based on the content of the character strings of each item after the recognition unit 13a has recognized it.

[0064] The discrimination unit 14 also discriminates the authenticity of the bill by determining the format of one or more recognized characters. This makes it possible to easily discern counterfeit bills with formal defects.

[0065] Furthermore, the discrimination unit 14 discriminates the authenticity of the bill by determining the consistency of one or more recognized characters. Therefore, it becomes possible to easily discern counterfeit bills with inconsistencies in consistency.

[0066] The discrimination unit 14 also discriminates the authenticity of the card by comparing one or more recognized characters with a predetermined character group. Therefore, in the case of an item in which a specific character group is written, such as the type field of a driver's license, if a character group other than the specific character group is written, it can be determined that the card is counterfeit.

[0067] Furthermore, if the accuracy information output from the recognition unit 13a is lower than a predetermined value, the discrimination unit 14 determines that the one or more recognized characters are forged. Therefore, when the characters on the face of a note are written in different fonts, for example, it is possible to determine that the face of the note is forged.

[0068] Exemplary embodiment 4 A fourth exemplary embodiment of the present invention will be described in detail with reference to the drawings. Note that components having the same functions as those described in the third exemplary embodiment are given the same reference numerals, and the description thereof will be omitted as appropriate.

[0069] (Configuration of Information Processing System) 8 is a block diagram showing a functional configuration of an information processing system 1 according to an exemplary embodiment 4. The information processing system 1 includes a reading device 30 and a server 40, which are connected via a communication network 50. The reading device 30 includes an imaging unit 31 and a communication unit 32. The imaging unit 31 corresponds to a camera, a scanner, etc. provided in a smartphone, and captures an image of the face of a bill.

[0070] The communication unit 32 communicates with a server 40 installed in a bank or the like via a communication network 50, which is a wide area communication network such as the Internet. The communication unit 32 transmits an image of the face of the bank note captured by the imaging unit 31 to the server 40 via the communication network 50.

[0071] The server 40 includes a communication unit 41 and an information processing device 10a. The communication unit 41 communicates with the reading device 30 via a communication network 50, which is a wide area communication network such as the Internet. The communication unit 41 receives an image of the face of the card from the reading device 30 via the communication network 50.

[0072] The acquisition unit 11 of the information processing device 10a acquires the image of the face of the card received by the communication unit 41. The subsequent processing is similar to that described in the exemplary embodiment 3. Therefore, detailed description will not be repeated.

[0073] The communication unit 41 receives the result of discrimination of the image of the card face by the information processing device 10 a, and transmits it to the reading device 30 via the communication network 50 .

[0074] 9 is a flowchart for explaining the processing procedure of the reading device 30 according to the fourth exemplary embodiment of the present invention. First, the imaging unit 31 captures an image of the face of the bill (S31). Then, the communication unit 32 transmits the image of the face of the bill captured by the imaging unit 31 to the server 40 via the communication network 50 (S32).

[0075] 10 is a flowchart for explaining the processing procedure of the server 40 according to the fourth exemplary embodiment of the present invention. First, the communication unit 41 receives an image of the face of the card from the reading device 30 via the communication network 50 (S41).

[0076] The information processing device 10a determines whether the face of the card is genuine (S42). The communication unit 41 then receives the result of the determination of the image of the face of the card by the information processing device 10a, and transmits it to the reading device 30 via the communication network 50 (S43).

[0077] As described above, the reading device 30 captures an image of the face of the note and transmits it to the server 40, and the server 40 determines whether the face of the note is genuine and transmits the determination result to the reading device 30. Therefore, it is no longer necessary for the reading device 30 to determine whether the face of the note is genuine.

[0078] In addition, since the cut-out unit 12 cuts out one or more target characters from the image of the face of the card, including the image surrounding the one or more target characters, the characters can be identified more accurately than if the recognition process were performed on only the target characters.

[0079] [Software implementation example] Some or all of the functions of the information processing devices 10, 10a, and 20 may be realized by hardware such as an integrated circuit (IC chip), or may be realized by software.

[0080] In the latter case, the information processing devices 10, 10a, and 20 are realized by, for example, a computer that executes instructions of a program, which is software that realizes each function. An example of such a computer (hereinafter, referred to as a computer 60) is shown in FIG. 11. The computer 60 includes at least one processor 61 and at least one memory 62, which are connected via an internal bus 63. The memory 62 stores a program P for operating the computer 60 as the information processing devices 10, 10a, and 20. In the computer 60, the processor 61 reads the program P from the memory 62 and executes it, thereby realizing each function of the information processing devices 10, 10a, and 20.

[0081] The processor 61 may be, for example, a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a microcontroller, a general-purpose computing on graphics processing units (GPGPU), or a combination thereof. The memory 62 may be, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or a combination thereof.

[0082] The computer 60 may further include a RAM (Random Access Memory) for expanding the program P during execution and for temporarily storing various data. The computer 60 may further include a communication interface for transmitting and receiving data to and from other devices. The computer 60 may further include an input / output interface for connecting input / output devices such as a keyboard, a mouse, a display, and a printer.

[0083] The program P can also be recorded on a non-transitory tangible recording medium 70 that can be read by the computer 60. For example, a CD-ROM (Compact Disc-Read Only Memory), a DVD (Digital Versatile Disc), a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit can be used as such a recording medium 70. The computer 60 can obtain the program P via such a recording medium 70. The program P can also be transmitted via a transmission medium. For example, a communication network or a broadcast wave can be used as such a transmission medium. The computer 60 can also obtain the program P via such a transmission medium.

[0084] [Additional Note 1] The present invention is not limited to the exemplary embodiments described above, and various modifications are possible within the scope of the claims. For example, exemplary embodiments obtained by appropriately combining the technical means disclosed in the exemplary embodiments described above are also included in the technical scope of the present invention.

[0085] [Additional Note 2] Some or all of the above-described exemplary embodiments may be described as follows. However, the present invention is not limited to the following described aspects.

[0086] (Appendix 1) An acquisition means for acquiring an image of a face of a card; A cutting means for cutting out one or more target characters from the image, including an image surrounding the one or more target characters; and a classification means for executing a classification process on the one or more target characters by inputting the image extracted by the extraction means to a trained model.

[0087] (Appendix 2) The information processing device according to claim 1, wherein the surrounding image is a ruled line surrounding the one or more target characters.

[0088] (Appendix 3) The information processing device according to claim 2, wherein the surrounding image is at least one of a ruled line arranged above the one or more target characters and a ruled line arranged below the one or more target characters.

[0089] (Appendix 4) The information processing device according to claim 1, wherein the surrounding image is at least one character other than the one or more target characters.

[0090] (Appendix 5) The model includes a plurality of models each trained corresponding to a plurality of items included on the face of the card, The identification means is identifying an item contained on the face of the card based on its location within an image of the face of the card; selecting a model corresponding to the specified item from among the plurality of models; The information processing device according to any one of appendices 1 to 4, further comprising: executing, using a selected model, a classification process for one or more characters included in the specified item.

[0091] (Appendix 6) The information processing device further comprises: 6. The information processing device according to any one of claims 1 to 5, further comprising a discrimination means for discriminating the authenticity of the face of the bill by referring to a result of the discrimination process.

[0092] (Appendix 7) a result of the classification process including one or more classified characters; The information processing device described in Appendix 6, wherein the discrimination means determines the authenticity of the face of the certificate by determining the format of the one or more recognized characters.

[0093] (Appendix 8) a result of the classification process including one or more classified characters; The information processing device according to claim 6 or 7, wherein the discrimination means determines the authenticity of the face of the certificate by determining the consistency of the one or more recognized characters.

[0094] (Appendix 9) a result of the classification process including one or more classified characters; The information processing device according to any one of Appendices 6 to 8, wherein the discrimination means discriminates the authenticity of the face of the note by comparing the one or more identified characters with a predetermined character group.

[0095] (Appendix 10) The result of the identification process includes accuracy information of the identification process, The information processing device according to any one of appendices 6 to 9, wherein the discrimination means discriminates whether the face of the bill is genuine or counterfeit by referring to the accuracy information.

[0096] (Appendix 11) a training data acquisition unit that acquires training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and a correct answer label for the one or more target characters; The information processing device according to any one of appendices 1 to 10, further comprising a learning unit that uses the teacher data to learn the model.

[0097] (Appendix 12) 12. The information processing device according to any one of appendices 1 to 11, wherein the face of the card is an identity verification document and is any one of a driver's license, a My Number card, a passport, and a health insurance card.

[0098] (Appendix 13) a training data acquisition unit that acquires training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and a correct answer label for the one or more target characters; An information processing device comprising: a learning unit that inputs an image obtained by cutting out one or more target characters together with images surrounding the one or more target characters from an image of a card face, and uses the training data to train a model that outputs a recognition result for the one or more target characters.

[0099] (Appendix 14) Take an image of the card, Extracting one or more target characters from the image, including images surrounding the one or more target characters; An information processing method comprising: inputting the extracted image into a trained model, and performing a classification process on the one or more target characters.

[0100] (Appendix 15) Acquire training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and a correct answer label for the one or more target characters; An information processing method in which an image obtained by cutting out one or more target characters from an image of a card face together with an image surrounding the one or more target characters is input, and a model that outputs a recognition result for the one or more target characters is trained using the training data.

[0101] (Appendix 16) Acquire training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and a correct answer label for the one or more target characters; A method for manufacturing a trained model, which uses an image obtained by cutting out one or more target characters from an image of a card face together with an image surrounding the one or more target characters as input, and trains a model using the training data to output a recognition result for the one or more target characters.

[0102] (Appendix 17) A computer program for causing a computer to execute an information processing method, The information processing method includes acquiring an image of a face of a card; Extracting one or more target characters from the image, including an image surrounding the one or more target characters; A computer program that executes a classification process for the one or more target characters by inputting the extracted image to a trained model.

[0103] (Appendix 18) A computer program for causing a computer to execute an information processing method, The information processing method includes acquiring training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and a correct label for the one or more target characters; A computer program that uses as input an image obtained by cutting out one or more target characters from an image of a card face, together with an image surrounding the one or more target characters, and uses the training data to train a model that outputs an identification result for the one or more target characters.

[0104] (Appendix 19) A computer-readable recording medium having a program recorded thereon that causes a computer to function as an information processing device, The computer includes an acquisition means for acquiring an image of a face of a bill; A cutting means for cutting out one or more target characters from the image, including an image surrounding the one or more target characters; a classification means for executing a classification process for the one or more target characters by inputting the image extracted by the extraction means to a trained model; A recording medium on which a program that functions as a

[0105] (Appendix 20) A computer-readable recording medium having a program recorded thereon that causes a computer to function as an information processing device, The computer includes a training data acquisition means for acquiring training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and a correct label for the one or more target characters; a learning means for learning a model using the training data, the model inputting an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and outputting a recognition result for the one or more target characters; A recording medium on which a program that functions as a

[0106] (Appendix 21) The method includes at least one processor, the processor comprising: A process of extracting one or more target characters from the image, including an image surrounding the one or more target characters; and performing a process of performing a classification process on the one or more target characters by inputting the image extracted by the extraction means to a trained model.

[0107] The information processing device may further include a memory, and the memory may store a program for causing the processor to execute the acquiring process, the extracting process, and the executing process. The program may be recorded in a computer-readable, non-transitory, tangible recording medium.

[0108] (Appendix 22) The method includes: acquiring training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and a correct label for the one or more target characters; An information processing device that inputs an image obtained by cutting out one or more target characters from an image of a card face together with images surrounding the one or more target characters, and uses the training data to train a model that outputs a recognition result for the one or more target characters.

[0109] The information processing device may further include a memory, and the memory may store a program for causing the processor to execute the acquiring process and the learning process. The program may be recorded in a computer-readable, non-transitory, tangible recording medium. [Explanation of symbols]

[0110] 1. Information Processing Systems 10, 10a, 20 Information processing device 11 Acquisition Department 12 Cutout 13, 13a Identification section 14 Discrimination part 21 Teacher Data Acquisition Department 22 Teacher data storage unit 23 Learning Department 30 Reading device 31 Imaging unit 32,41 Communications Department 40 Servers 50 Communication Network 60 Computer 61 processors 62 Memory 63 Internal Bus 70 Recording media 131 Name Model 132 Address Model Model for 133 types P Program

Claims

1. An acquisition means for acquiring an image of a face of a card; A cutting means for cutting out one or more object characters from the image together with an image surrounding the one or more object characters; A classification unit that executes a classification process for the one or more target characters by inputting the image extracted by the extraction unit to a trained model, the peripheral image is a ruled line around the one or more target characters, The information processing device, wherein the trained model is a model that has been machine-learned to learn an image including the one or more target characters and the ruled lines.

2. The information processing device according to claim 1 , wherein the peripheral image is at least one of a ruled line arranged above the one or more object characters and a ruled line arranged below the one or more object characters.

3. The information processing device according to claim 1 , wherein the surrounding image is at least one character other than the one or more target characters.

4. The model includes a plurality of models each trained corresponding to a plurality of items included on the face of the card, The identification means is identifying an item contained on the face of the card based on its location within an image of the face of the card; selecting a model corresponding to the specified item from among the plurality of models; The information processing apparatus according to claim 1 , further comprising: a step of executing, using a selected model, a classification process for one or more characters included in the specified item.

5. The information processing device further comprises:

5. The information processing device according to claim 1, further comprising a discrimination means for discriminating the authenticity of said face of said bill by referring to a result of said discrimination process.

6. a result of the classification process including one or more classified characters; The information processing apparatus according to claim 5 , wherein the determining means determines whether the face of the card is genuine by determining a format of the one or more identified characters.

7. The result of the classification process includes one or more classified characters, The information processing device described in claim 5, wherein the discrimination means determines that the face of the card is genuine when the content of the one or more identified characters and the content of identified characters contained on the face of the card that are different from the one or more identified characters satisfy predetermined conditions.

8. The trained model is trained to output accuracy information of the one or more target characters by inputting an image cut out by the cut-out means, The information processing device according to claim 5 , wherein the determining means determines that the face of the card is counterfeit when the certainty information is lower than a predetermined value.

9. A training data acquisition means for acquiring training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and a correct answer label for the one or more target characters; a learning means for learning a model using the training data, the model receiving an input of an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and outputting a recognition result for the one or more target characters; the peripheral image is a ruled line around the one or more target characters, The model is a model that is machine-learned to learn an image including the one or more target characters and the ruled lines.

10. Take an image of the card, Extracting one or more target characters from the image, including images surrounding the one or more target characters; Executing a classification process for the one or more target characters by inputting the extracted image to a trained model; the peripheral image is a ruled line around the one or more target characters, An information processing method, wherein the trained model is a model trained by machine learning on an image including the one or more target characters and the ruled lines.

11. Acquire training data including a plurality of pairs of an image obtained by cutting out one or more target characters together with an image surrounding the one or more target characters from an image of a face of a card, and a correct answer label for the one or more target characters; an image obtained by cutting out one or more target characters from an image of a face of a card together with an image surrounding the one or more target characters is input, and a model that outputs a recognition result for the one or more target characters is trained using the training data; the peripheral image is a ruled line around the one or more target characters, An information processing method, wherein the model is a model that has been machine-learned to learn an image including the one or more target characters and the ruled lines.

Images