Review System

Abstract

Provided is a review system with which it is easy for a user to determine whether or not a typographical error or the like has been made. Specifically provided is a review system that divides sentences included in a group of comparison documents into a plurality of first words, and performs review using a group of comparison images obtained by imaging the first words. Specifically, first, the review system divides sentences included in a specified document into a plurality of second words, and acquires the frequencies of occurrence of the plurality of second words in the group of comparison documents. Next, the review system acquires a verification image by imaging a second word among the plurality of second words that has a frequency of occurrence equal to or less than a threshold value. The review system then obtains the degrees of similarity between the verification image and the comparison images included in the group of comparison images, and presents the first word represented by the comparison image having at least the highest degree of similarity among the comparison images. The presentation is made, for example, by indicating that there is a possibility that the second word represented by the verification image may be a typographical error for the first word represented by a comparison image having a high degree of similarity to the verification image.

Description

[Technical Field]

[0001] One aspect of the present invention relates to a document review system and a document review method.

[0002] One embodiment of the present invention is not limited to the above technical field, and examples of the technical field of one embodiment of the present invention include semiconductor devices, display devices, light-emitting devices, power storage devices, memory devices, electronic devices, lighting devices, input devices (e.g., touch sensors), input / output devices (e.g., touch panels), driving methods thereof, and manufacturing methods thereof. [Background technology]

[0003] When entering a word and searching the entire document for the location where that word appears, if the document contains a typo, the word may not be found even if it is the same as the entered word. For example, if the word for "system" is entered in the document as "systm" including a typo, "systm" will not be found even if "system" is entered as the word to be searched. Therefore, if typos can be detected, the typo can be corrected or the search can be performed taking the typo into consideration, thereby increasing the comprehensiveness of the search. As a method for detecting typos, a method has been disclosed in which words contained in the document to be searched are sorted and similar but different words are displayed as words that may be typos (Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2014 / 171519 Summary of the Invention [Problem to be solved by the invention]

[0005] In the method disclosed in Patent Document 1, the user makes the final decision on whether or not a character is a typo. However, in cases where a character difference is difficult for a human to distinguish at a glance, such as between "T" (alphabet) and "T" (Greek letter), it is difficult to determine whether or not a character is a typo. However, for example, even though "T" (alphabet) and "T" (Greek letter) look similar, they have different character codes, so a computer recognizes them as different characters. Therefore, for example, if a character that should be written as "T" (alphabet) is written as "T" (Greek letter), the comprehensiveness of the search decreases, just as in cases where a typo that can be determined to be a typo at a glance is included. Therefore, it is preferable to enable the user to determine whether or not a character difference is difficult for a human to distinguish at a glance.

[0006] An object of one embodiment of the present invention is to provide a proofreading system or a proofreading method that allows a user to easily determine whether or not a text contains a clerical error, etc. Alternatively, an object of one embodiment of the present invention is to provide a proofreading system or a proofreading method that is highly convenient. Alternatively, an object of one embodiment of the present invention is to provide a proofreading system or a proofreading method that can detect clerical errors, etc. with high accuracy. Alternatively, an object of one embodiment of the present invention is to provide a novel proofreading system or a proofreading method.

[0007] Note that the description of these problems does not preclude the existence of other problems. One embodiment of the present invention does not necessarily have to solve all of these problems. Problems other than these can be extracted from the description in the specification, drawings, and claims. [Means for solving the problem]

[0008] One aspect of the present invention is a proofreading system having a division unit, an occurrence frequency acquisition unit, an image generation unit, a similarity acquisition unit, and a presentation unit, wherein the division unit has the function of dividing sentences included in a comparison document group into multiple first words and the function of dividing sentences included in a specified document into multiple second words, the occurrence frequency acquisition unit has the function of acquiring the occurrence frequency of the multiple second words in the comparison document group, the image generation unit has the function of imaging the first words to acquire a comparison image group, and the image generation unit has the function of imaging second words among the multiple second words whose occurrence frequency is below a threshold to acquire a verification image, the similarity acquisition unit has the function of acquiring the similarity between the verification image and a comparison image included in the comparison image group, and the presentation unit has the function of presenting the first word represented by at least the comparison image with the highest similarity among the comparison images.

[0009] Alternatively, one aspect of the present invention includes a division unit, an appearance frequency acquisition unit, an image generation unit, a similarity acquisition unit, a model calculation unit, and a presentation unit, wherein the division unit has a function of dividing a sentence included in a comparison document group into a plurality of first words and a function of dividing a sentence included in a specified document into a plurality of second words, the appearance frequency acquisition unit has a function of acquiring appearance frequencies of the plurality of second words in the comparison document group, the image generation unit has a function of imaging the first words to acquire a comparison image group, and the image generation unit has a function of acquiring the plurality of The proofreading system has a function of imaging second words whose frequency of occurrence is equal to or less than a first threshold value to obtain a verification image, a similarity acquisition unit has a function of acquiring the similarity between the verification image and a comparison image included in a group of comparison images, a model calculation unit has a function of acquiring the probability that a first word represented by a comparison image whose similarity is equal to or greater than the second threshold value will be replaced by a second word represented by the verification image, and a presentation unit has a function of presenting at least the first word with the highest probability.

[0010] Alternatively, in the above aspect, the model calculation unit may have a function of performing calculations using a machine learning model.

[0011] Alternatively, in the above aspect, the machine learning model may be one that has been trained using a group of comparison documents.

[0012] Alternatively, in the above aspect, the machine learning model may be a neural network model.

[0013] Alternatively, one aspect of the present invention is a proofreading system having a division unit, an occurrence frequency acquisition unit, an image generation unit, a model calculation unit, and a presentation unit, wherein the division unit has a function of dividing sentences included in a comparison document group into a plurality of first words and a function of dividing sentences included in a specified document into a plurality of second words, the occurrence frequency acquisition unit has a function of acquiring the occurrence frequency of the plurality of second words in the comparison document group, the image generation unit has a function of imaging the first words to acquire a comparison image group, and the image generation unit has a function of imaging second words among the plurality of second words whose occurrence frequency is below a first threshold to acquire a verification image, the model calculation unit has a function of estimating the word represented by the verification image, and the presentation unit has a function of presenting the result of the estimation.

[0014] Alternatively, in the above aspect, the model calculation unit may have a function of performing calculations using a machine learning model.

[0015] Alternatively, in the above aspect, the machine learning model may be one that has been trained using a group of comparison images.

[0016] Alternatively, in the above aspect, the machine learning model may be trained by supervised learning using data in which words are linked as correct labels to comparison images included in a comparison image group.

[0017] Alternatively, in the above aspect, the machine learning model may have a first classifier and two or more second classifiers, the first classifier having a function of grouping comparison images included in a group of comparison images, and the second classifier having a function of estimating the words represented by the grouped comparison images, and the estimation may be performed using a different second classifier for each group.

[0018] Alternatively, in the above aspect, the machine learning model may be a neural network model.

[0019] Alternatively, in the above aspect, the presentation unit may have a function of performing a display.

[0020] Alternatively, one aspect of the present invention is a proofreading method that uses a group of comparison images obtained by dividing a sentence included in a group of comparison documents into a plurality of first words and imaging the first words, the proofreading method dividing a sentence included in a specified document into a plurality of second words, obtaining the frequency of occurrence of the plurality of second words in the group of comparison documents, imaging a second word among the plurality of second words whose frequency of occurrence is below a threshold value to obtain a verification image, obtaining the similarity between the verification image and a comparison image included in the group of comparison images, and presenting the first word represented by at least the comparison image with the highest similarity among the comparison images.

[0021] Alternatively, one aspect of the present invention is a proofreading method that uses a group of comparison images obtained by dividing a sentence included in a group of comparison documents into a plurality of first words and imaging the first words, the proofreading method dividing a sentence included in a specified document into a plurality of second words, obtaining the frequency of occurrence of the plurality of second words in the group of comparison documents, imaging second words among the plurality of second words whose frequency of occurrence is below a threshold value to obtain a verification image, obtaining the similarity between the verification image and a comparison image included in the group of comparison images, obtaining the probability that a first word represented by a comparison image whose similarity is above a second threshold value can be replaced by a second word represented by the verification image, and presenting at least the first word with the highest probability.

[0022] Alternatively, in the above aspect, the probability may be obtained using a machine learning model.

[0023] Alternatively, in the above aspect, the machine learning model may be one that has been trained using a group of comparison documents.

[0024] Alternatively, in the above aspect, the machine learning model may be a neural network model.

[0025] Alternatively, one aspect of the present invention is a proofreading method that uses a group of comparison images obtained by dividing a sentence included in a group of comparison documents into a plurality of first words and imaging the first words, the proofreading method dividing a sentence included in a specified document into a plurality of second words, obtaining the frequency of occurrence of the plurality of second words in the group of comparison documents, imaging a second word among the plurality of second words whose frequency of occurrence is below a threshold value to obtain a verification image, inferring the word represented by the verification image, and presenting the result of the inference.

[0026] Alternatively, in the above aspect, the estimation may be performed using a machine learning model.

[0027] Alternatively, in the above aspect, the machine learning model may be one that has been trained using a group of comparison images.

[0028] Alternatively, in the above aspect, the machine learning model may be trained by supervised learning using data in which words are linked as correct labels to comparison images included in a comparison image group.

[0029] Alternatively, in the above aspect, the machine learning model may have a first classifier and two or more second classifiers, the first classifier having a function of grouping comparison images included in a group of comparison images, the second classifier having a function of estimating words represented by the grouped comparison images, and the estimation of words represented by the comparison images may be performed using different second classifiers for each group.

[0030] Alternatively, in the above aspect, the machine learning model may be a neural network model.

[0031] Alternatively, in the above aspect, the presentation may be performed by display. [Effects of the Invention]

[0032] One aspect of the present invention can provide a proofreading system or proofreading method that allows a user to easily determine whether or not a mistake is a clerical error, etc. Alternatively, one aspect of the present invention can provide a highly convenient proofreading system or proofreading method. Alternatively, one aspect of the present invention can provide a proofreading system or proofreading method that can detect clerical errors, etc. with high accuracy. Alternatively, one aspect of the present invention can provide a novel proofreading system or proofreading method.

[0033] Note that the description of these effects does not preclude the existence of other effects. One embodiment of the present invention does not necessarily have all of these effects. Effects other than these can be extracted from the description in the specification, drawings, and claims. [Brief explanation of the drawings]

[0034] FIG. 1 is a diagram illustrating an example of the configuration of a proofreading system. FIG. 2 is a diagram showing an example of a proofreading method. 3A to 3C are diagrams showing an example of a review method. FIG. 4 is a diagram showing an example of a proofreading method. 5A to 5E are diagrams showing an example of a review method. FIG. 6 is a diagram illustrating an example of the configuration of a proofreading system. FIG. 7 is a diagram showing an example of a proofreading method. FIG. 8 is a diagram illustrating an example of the configuration of a proofreading system. FIG. 9 is a diagram showing an example of a proofreading method. 10A and 10B are diagrams showing an example of a proofreading method. FIG. 11 is a diagram showing an example of a proofreading method. FIG. 12 is a diagram showing an example of a proofreading method. FIG. 13 is a diagram illustrating an example of a proofreading system. DETAILED DESCRIPTION OF THE INVENTION

[0035] The embodiments will be described in detail with reference to the drawings. However, the present invention is not limited to the following description, and it will be readily understood by those skilled in the art that various changes in form and details can be made without departing from the spirit and scope of the present invention. Therefore, the present invention should not be interpreted as being limited to the description of the embodiments shown below. In the configuration of the invention described below, the same parts or parts having similar functions will be denoted by the same reference numerals in different drawings, and repeated explanations will be omitted.

[0036] Furthermore, in this specification, ordinal numbers such as "first" and "second" are used to avoid confusion between components. Therefore, they do not limit the number of components. Furthermore, they do not limit the order of the components. For example, a component referred to as "first" in this specification may be a component referred to as "second" in the claims. Furthermore, a component referred to as "first" in this specification may be omitted in the claims.

[0037] (Embodiment) In this embodiment, a proofreading system and a proofreading method according to one embodiment of the present invention will be described with reference to drawings.

[0038] A proofreading system according to one embodiment of the present invention can distinguish between characters that look similar but have different character codes, such as "T" (an alphabetic character) and "T" (a Greek character). For example, if a document contains the word "FET" (F and E are alphabetic characters, and T is a Greek character), a user of the proofreading system can be informed that "FET" (F and E are alphabetic characters, and T is a Greek character) may be a misspelling of "FET" (F, E, and T are all alphabetic characters). Thus, the proofreading system according to one embodiment of the present invention can make it easier for users to find errors that are difficult to find visually.

[0039] Specifically, a group of documents for comparison is registered in a database. Sentences contained in the group of documents for comparison are divided into words, and the words are converted into images. These images are used as comparison images. The comparison images are also registered in the database.

[0040] In this state, the designated document to be proofread is input into a proofreading system according to one embodiment of the present invention. Of the words contained in the designated document, those that appear less frequently in the comparison document group are deemed to be possible typos. These words are converted into images and used as verification images. The similarity between the verification image and the comparison image is obtained. The proofreading system according to one embodiment of the present invention can present information indicating that the word represented in the verification image is likely to be a typo of the word represented in the comparison image with a high similarity.

[0041] <Proofreading System_1> 1 is a block diagram showing an example configuration of a proofreading system 10a. The proofreading system 10a includes a reception unit 11, a storage unit 12, a processing unit 13, and a presentation unit 14. The processing unit 13 includes a division unit 21, an appearance frequency acquisition unit 22, an image generation unit 23, and a similarity acquisition unit 24.

[0042] In Figure 1, arrows indicate the exchange of data, etc. between the components of the proofreading system 10a. Note that the exchange of data, etc. shown in Figure 1 is an example, and there are cases where data, etc. can be exchanged between components that are not connected by arrows. Also, there are cases where data, etc. is not exchanged between components that are connected by arrows. The same applies to block diagrams other than Figure 1.

[0043] The review system 10a may be provided in an information processing device such as a personal computer (PC) used by a user, or the storage unit 12 and processing unit 13 of the review system 10a may be provided in a server, and the review system 10a may be accessed and used from a client PC via a network.

[0044] In this specification, a user of a device or equipment in which a system such as a proofreading system is installed may be simply referred to as a “user of the system.” For example, a user of an information processing device in which a proofreading system is installed may be referred to as a user of the proofreading system.

[0045] [Reception Section 11] The receiving unit 11 has a function of receiving a document. Specifically, the receiving unit 11 has a function of receiving data representing a document. The document supplied to the receiving unit 11 can be supplied to the processing unit 13.

[0046] Unless otherwise specified herein, a document refers to a description of an event in natural language. A document is computerized and machine-readable. Examples of documents include, but are not limited to, patent applications, utility model applications, design applications, trademark applications, legal precedents, contracts, terms and conditions, product manuals, novels, publications, white papers, and technical documents.

[0047] [Storage unit 12] The storage unit 12 has a function of storing data supplied to the reception unit 11, data output from the processing unit 13, etc. The storage unit 12 also has a function of storing a program executed by the processing unit 13.

[0048] The storage unit 12 includes at least one of a volatile memory and a nonvolatile memory. Examples of the volatile memory include a dynamic random access memory (DRAM) and a static random access memory (SRAM). Examples of the nonvolatile memory include a resistive random access memory (ReRAM), a phase change random access memory (PRAM), a ferroelectric random access memory (FeRAM), a magnetoresistive random access memory (MRAM), and a flash memory. The storage unit 12 may also include a recording media drive. Examples of the recording media drive include a hard disk drive (HDD) and a solid state drive (SSD).

[0049] The storage unit 12 may have a database. For example, an application database may be used as the database. The applications may include intellectual property applications such as patent applications, utility model registration applications, design registration applications, and trademark registration applications. There are no limitations on the status of each application, and it does not matter whether the application is published, pending at the Patent Office, or registered. For example, the application database may include at least one of pre-examination applications, applications under examination, and registered applications, or may include all of them.

[0050] For example, the application database preferably includes one or both of the specifications and claims of a plurality of patent applications or utility model registration applications. The specifications and claims are stored, for example, as text data.

[0051] The application database may include at least one of an application management number (including a unique internal number) for identifying an application, an application family management number for identifying an application family, an application number, a publication number, a registration number, drawings, an abstract, a filing date, a priority date, a publication date, a status, a classification (such as a patent classification or a utility model classification), a category, and keywords. Each of these pieces of information may be used to identify a document when the receiving unit 11 receives the document. Alternatively, each of these pieces of information may be output together with the processing result of the processing unit 13.

[0052] In addition, various types of documents, such as books, magazines, newspapers, and papers, can be managed in the database. The database contains at least text data of the documents. The database may further contain at least one of a number identifying each document, a title, a date such as a publication date, an author, and a publisher. Each of these pieces of information may be used to identify the document when it is accepted. Alternatively, each of these pieces of information may be output together with the processing results of the processing unit 13.

[0053] Review system 10a may have a function to retrieve data such as documents from a database located outside the system. Also, review system 10a may have a function to retrieve data from both a database stored in storage unit 12 and a database located outside review system 10a.

[0054] Alternatively, instead of a database, one or both of a storage and a file server may be used. For example, if the review system 10a uses files stored in a file server, it is preferable that the storage unit 12 stores the paths of the files stored in the file server.

[0055] [Processing section 13] The processing unit 13 has a function of performing processing such as calculations using the data supplied from the reception unit 11 and the data stored in the storage unit 12. The processing unit 13 can supply the processing result to the storage unit 12 or the presentation unit 14.

[0056] The processing unit 13 may have, for example, a central processing unit (CPU). The processing unit 13 may have a microprocessor such as a digital signal processor (DSP) and a graphics processing unit (GPU). The microprocessor may be implemented by a programmable logic device (PLD) such as a field programmable gate array (FPGA) and a field programmable analog array (FPAA). The processing unit 13 can perform various data processing and program control by interpreting and executing instructions from various programs using the processor. Programs that can be executed by the processor are stored in at least one of a memory area of ​​the processor and the storage unit 12.

[0057] The processing unit 13 may have a main memory, which includes at least one of a volatile memory such as a random access memory (RAM) and a non-volatile memory such as a read only memory (ROM).

[0058] The RAM may be, for example, a DRAM or an SRAM, and a virtual memory space is allocated and used as a working space for the processing unit 13. The operating system, application programs, program modules, program data, lookup tables, etc. stored in the storage unit 12 are loaded into the RAM for execution. The data, programs, and program modules loaded into the RAM are each directly accessed and operated by the processing unit 13.

[0059] ROM can store BIOS (Basic Input / Output System) and firmware, which do not require rewriting. Examples of ROM include mask ROM, OTPROM (One-Time Programmable Read Only Memory), and EPROM (Erasable Programmable Read Only Memory). Examples of EPROM include UV-EPROM (Ultra-Violet Erasable Programmable Read Only Memory), which allows stored data to be erased by exposure to ultraviolet light, EEPROM (Electrically Erasable Programmable Read Only Memory), and flash memory.

[0060] The components of the processing unit 13 will be described below.

[0061] ≪Divided part 21≫ The segmentation unit 21 has a function of segmenting sentences included in a document into words. For example, an English sentence can be segmented into words based on spaces. Furthermore, a Japanese sentence can be segmented into words by, for example, performing word segmentation processing. The words acquired by the segmentation unit 21 can be supplied to the occurrence frequency acquisition unit 22, the image generation unit 23, and the similarity acquisition unit 24. Here, when segmenting a sentence into words, the segmentation unit 21 preferably performs a cleaning process on the sentence. The cleaning process removes noise contained in the sentence. For example, in the case of an English sentence, the cleaning process can include deleting semicolons and replacing colons with commas.

[0062] The division unit 21 also has a function of performing, for example, morphological analysis on the divided words, thereby making it possible to determine the part of speech of the words.

[0063] The division unit 21 does not necessarily divide the sentences included in the document into individual words. For example, the division unit 21 may divide some words into compound words. In other words, one divided word may contain two or more words.

[0064] <<Appearance frequency acquisition unit 22>> The occurrence frequency acquisition unit 22 has a function of acquiring the occurrence frequency of the words acquired by the division unit 21 by dividing a sentence, for example, in a group of documents registered in a database. Specifically, the occurrence frequency acquisition unit 22 can acquire the frequency at which words having the same character code as the character code representing the words acquired by the division unit 21 by dividing a sentence appear in the group of documents registered in the database. Here, the group of documents represents a set of one or more documents. The group of documents may include, for example, all or part of the documents registered in the database. For example, if technical documents such as patent applications or papers are registered in the database, the group of documents may be a set of documents in a specific technical field among the documents registered in the database.

[0065] The occurrence frequency acquisition unit 22 can acquire the occurrence frequency of a word, for example, as a TF (Term Frequency) value. The occurrence frequency acquired by the occurrence frequency acquisition unit 22 can be supplied to, for example, the storage unit 12 and registered in a database, and can also be supplied to the image generation unit 23.

[0066] <Image generation unit 23> The image generation unit 23 has a function of generating image data that visualizes words. The image can be binary data, for example, with the text representing the words in white and the background in black. The image may also be binary data, for example, with the text representing the words in black and the background in white. Furthermore, the image may be multi-valued data. For example, the text representing the words may be gray and the background may be black or black. Alternatively, the text representing the words may be white or black and the background may be gray. Furthermore, the image may be a color image.

[0067] Specifically, the image generation unit 23 can visualize the words acquired by the division unit 21. Here, the image generation unit 23 does not need to visualize all of the words acquired by the division unit 21. For example, the image generation unit 23 can visualize, among the words acquired by the division unit 21, words whose occurrence frequency acquired by the occurrence frequency acquisition unit 22 is equal to or less than a threshold value.

[0068] The image acquired by the image generating unit 23 can be supplied to, for example, the storage unit 12 and registered in a database, and can also be supplied to the similarity acquiring unit 24 .

[0069] ≪Similarity acquisition unit 24≫ The similarity acquisition unit 24 has a function of comparing the images acquired by the image generation unit 23 and acquiring the similarity. The similarity can be calculated and acquired by, for example, region-based matching or feature-based matching. The similarity acquisition unit 24 also has a function of selecting words to be supplied to the presentation unit 14 based on the similarity. Here, the division unit 21 performs the cleaning process described above, so that the similarity can be calculated with high accuracy.

[0070] In this specification, the term "calculate" refers to, for example, performing a mathematical operation. The term "obtain" includes the meaning of the term "calculate," but does not necessarily involve a mathematical operation. For example, when A reads data from a database, it can be said that A obtains data.

[0071] [Presentation part 14] The presentation unit 14 has a function of presenting information to the user of the review system 10a based on the processing result of the processing unit 13. The information can be, for example, words output by the similarity acquisition unit 24. The presentation unit 14 can present the information to the user of the review system 10a, for example, by displaying the information. That is, the presentation unit 14 can be, for example, a display. The presentation unit 14 may also have a function as a speaker.

[0072] The proofreading system 10a can be used to proofread for clerical errors, etc. For example, a group of documents for comparison is registered in a database held by the storage unit 12. Furthermore, sentences included in the group of documents for comparison are divided into words by the division unit 21, and the words are converted into images by the image generation unit 23. Such images are used as comparison images. The comparison images are also registered in the database.

[0073] In this state, the designated document, which is the document to be proofread, is supplied to the reception unit 11. Of the words contained in the designated document, words that appear less frequently in the comparison document group are deemed to be possible typos. Such words are imaged by the image generation unit 23 to be used as a verification image. The similarity between the verification image and the comparison image is acquired by the similarity acquisition unit 24. The word represented by the verification image and the word represented by the comparison image with high similarity are supplied to the presentation unit 14. The presentation unit 14 can present the fact that the word represented by the verification image is likely to be a typo of the word represented by the comparison image with high similarity.

[0074] As described above, the proofreading system 10a can distinguish between characters that look similar but have different character codes, such as "T" (an alphabetic character) and "T" (a Greek character). For example, if a specified document contains the word "FET" (F and E are alphabetic characters, and T is a Greek character), the proofreading system 10a can notify a user that "FET" (F and E are alphabetic characters, and T is a Greek character) may be a misspelling of "FET" (F, E, and T are all alphabetic characters). Thus, the proofreading system 10a can easily detect errors that are difficult for users to detect visually. Therefore, one aspect of the present invention can provide a proofreading system and a proofreading method that allow users to easily determine whether a character is a misspelling or not. Furthermore, one aspect of the present invention can provide a highly convenient proofreading system and a proofreading method.

[0075] The proofreading system 10a can also be used to correct characters read by optical character recognition (OCR). For example, suppose a document containing "FET" (F, E, and T are all letters of the alphabet) is read by OCR, but is recognized as "FET" (F and E are letters of the alphabet, and T is a Greek letter). In this case, by designating the document read by OCR as the specified document, the proofreading system 10a can correct "FET" (F and E are letters of the alphabet, and T is a Greek letter) to "FET" (F, E, and T are all letters of the alphabet).

[0076] An example of a proofreading method using the proofreading system 10a will be described below with reference to FIGS.

[0077] <Review Method_1> First, data required for the review system 10a to have the function of performing review is acquired and registered in, for example, a database. As described above, the database can be included in the storage unit 12. Alternatively, the database can be a database that exists outside the review system 10a.

[0078] FIG. 2 is a flowchart showing an example of a method for acquiring data required for the review system 10a to have a function of performing review, and includes processes from step S01 to step S05.

[0079] [Step S01] In step S01, the receiving unit 11 receives a comparison document group 100. Fig. 3A is a schematic diagram showing an example of the processing in step S01. As shown in Fig. 3A, the comparison document group 100 is a collection of one or more comparison documents 101.

[0080] The comparison document group 100 includes, for example, all or some of the documents registered in a database as the comparison documents 101. It is preferable that the comparison document group 100 includes many comparison documents 101 from the same field as the designated document to be proofread, since this allows the proofreading system 10a to detect clerical errors and the like with high accuracy. For example, if the designated document is a technical document such as a patent application or a paper, it is preferable that the comparison document 101 is also a technical document such as a patent application or a paper. Furthermore, if the designated document is a technical document in the electrical field, it is preferable that the comparison document 101 is also a technical document in the electrical field. Furthermore, if the designated document is a technical document in the semiconductor field, it is preferable that the comparison document 101 is also a technical document in the semiconductor field.

[0081] [Step S02] In step S02, the segmentation unit 21 acquires a comparison term group 102 by segmenting sentences included in the comparison document 101 into words. FIG. 3B is a schematic diagram illustrating an example of the processing in step S02. As shown in FIG. 3B, the comparison term group 102 can be a collection of words 103. FIG. 3B illustrates an example in which the comparison document 101 includes the word "FET." In this case, the words 103 included in the comparison term group 102 also include "FET." Here, if the same word appears multiple times in the comparison document group 100, the comparison term group 102 is also deemed to include multiple instances of the same word 103. For example, if the word "FET" appears 100 times in the comparison document group 100, the comparison term group 102 is deemed to include 100 instances of the word "FET" 103.

[0082] As described above, for example, an English sentence can be divided into words based on spaces. Also, a Japanese sentence can be divided into words by, for example, performing word segmentation. When dividing into words, for example, morphological analysis can be performed.

[0083] Here, it is preferable to unify the font of the text representing the words 103 included in the comparison term group 102. Furthermore, for one word, multiple words with different text fonts may be prepared as words 103 included in the comparison term group 102.

[0084] [Step S03] In step S03, the occurrence frequency obtaining unit 22 calculates and obtains the occurrence frequency of the term 103 in the comparison document set 100. As described above, the occurrence frequency can be calculated as, for example, a TF value.

[0085] Here, it is not necessary to acquire the occurrence frequency for all words 103. For example, when morphological analysis is performed, it is possible to acquire the occurrence frequency only for words 103 of a specific part of speech. In an English sentence, for example, it is possible to acquire the occurrence frequency for nouns, but not for articles. Also, in a Japanese sentence, it is possible to acquire the occurrence frequency for nouns, but not for particles.

[0086] [Step S04] In step S04, the image generation unit 23 visualizes the words 103 included in the comparison term group 102 to obtain a comparison image group 104. FIG. 3C is a schematic diagram showing an example of the processing in step S04. As shown in FIG. 3C, the comparison image group 104 can be a collection of comparison images 105 obtained by visualizing the words 103. FIG. 3C shows an example in which the comparison images 105 are binary data in which the text representing the words 103 is colored white and the background is colored black.

[0087] In step S04, for example, the words 103 whose frequency of appearance in the comparison document set 100 is obtained in step S03 can be converted into comparison images 105. Here, only one of the duplicated words 103 can be imaged. For example, even if the comparison term set 102 contains 100 occurrences of the word "FET" 103, only one of the words "FET" 103 can be imaged.

[0088] Note that step S03 and step S04 can be performed in parallel. That is, acquisition of the occurrence frequency by the occurrence frequency acquisition unit 22 and visualization of the word 103 by the image generation unit 23 can be performed in parallel. Also, step S04 may be performed after step S03, or step S03 may be performed after step S04.

[0089] [Step S05] In step S05, the occurrence frequency of word 103 acquired by occurrence frequency acquisition unit 22 in step S03 and the group of comparison images 104 acquired by image generation unit 23 in step S04 are registered in, for example, a database. As described above, the database can be, for example, a database included in storage unit 12. Alternatively, the occurrence frequency and group of comparison images 104 may be registered in a database external to review system 10a. Note that if review system 10a does not perform steps S03 and S04 in parallel, but instead performs step S04 after step S03, step S03 can be performed so that occurrence frequency acquisition unit 22 acquires the occurrence frequency of word 103 and registers it in the database, and then step S04 can be performed so that image generation unit 23 acquires group of comparison images 104 and registers it in the database.

[0090] As a result, the proofreading system 10a can have a proofreading function.

[0091] FIG. 4 is a flowchart showing an example of a proofreading method by the proofreading system 10a, and includes processes from step S11 to step S16.

[0092] [Step S11] In step S11, the receiving unit 11 receives a designated document 111, which is a document to be proofread. Fig. 5A is a schematic diagram showing an example of the processing in step S11. In Fig. 5A, the designated document 111 is a single document. Note that the receiving unit 11 may receive a plurality of documents as the designated document 111.

[0093] A user of the proofreading system 10a can directly input the designated document 111 to the reception unit 11. The designated document 111 can also be, for example, a document registered in a database. For example, if the designated document 111 is a document registered in a database, the user of the proofreading system 10a can identify the designated document 111 by inputting information that identifies the document (for example, by searching the database). Examples of information that identifies a document include a number that identifies the document and a title.

[0094] Furthermore, if the user of the proofreading system 10a wishes to proofread a part of a document (for example, a specific chapter), the user may designate the part of the document as the designated document 111.

[0095] [Step S12] In step S12, the dividing unit 21 divides a sentence included in the designated document 111 into words, thereby acquiring the designated document word group 112. FIG. 5B is a schematic diagram showing an example of the processing in step S12. As shown in FIG. 5B, the designated document word group 112 can be a set of words 113. FIG. 5B shows an example in which the designated document 111 contains, for example, one word, "FET" (F and E are alphabets, and T is a Greek letter). In this case, the words 113 included in the designated document word group 112 also contain "FET" (F and E are alphabets, and T is a Greek letter).

[0096] As described above, for example, an English sentence can be divided into words 113 based on spaces. Also, a Japanese sentence can be divided into words 113 by, for example, performing word segmentation processing. When dividing into words 113, for example, morphological analysis may be performed to determine the part of speech of the words 113.

[0097] Here, when the segmentation unit 21 performs, for example, morphological analysis, if the designated document 111 contains a typographical error, it may not be possible to determine the part of speech for the word containing the typographical error. For example, it may not be possible to determine that "FET" (F and E are alphabets, and T is a Greek letter) is a noun. In other words, when segmenting a sentence contained in the designated document 111 into words, it is preferable to perform, for example, morphological analysis, so that words that may have typos can be detected in step S12.

[0098] Furthermore, it is preferable that the font of the text representing the word 113 included in the designated document term group 112 is the same as the font of the text representing the word 103 included in the comparison term group 102. Therefore, if the font of the text representing the word 113 is different from the font of the text representing the word 103, it is preferable that the division unit 21 converts the font of the text representing the word 113.

[0099] [Step S13] In step S13, the occurrence frequency acquisition unit 22 acquires the occurrence frequency in the comparison document group 100 of the word 113 included in the designated document word group 112. The occurrence frequency can be acquired, for example, by reading it from a database or by reading it from the storage unit 12. For example, the occurrence frequency in the comparison document group 100 of a word 103 having the same character code as the character code representing the word 113 can be set as the occurrence frequency of the word 113 in the comparison document group 100. In this case, the word 113 whose occurrence frequency cannot be acquired can be set as a word that does not appear in the comparison document group 100. Therefore, the occurrence frequency in the comparison document group 100 of the word 113 whose occurrence frequency cannot be acquired can be set to 0. Note that in step S13, the occurrence frequency acquisition unit 22 may calculate the occurrence frequency in the comparison document group 100 of the word 113 included in the designated document word group 112. In this case, the occurrence frequency of the word 103 in the comparison document group 100 does not need to be registered in, for example, a database. Therefore, for example, step S03 shown in FIG. 2 can be omitted.

[0100] Here, it is not necessary to acquire the occurrence frequency for all words 113. For example, when morphological analysis is performed in step S12, the occurrence frequency of words 113 whose parts of speech cannot be determined is likely to be low in the comparison document group 100. Therefore, the occurrence frequency acquisition unit 22 does not need to acquire the occurrence frequency for words 113 whose parts of speech cannot be determined.

[0101] A word 113 that appears infrequently in the comparison document group 100 can be considered to be a possible typo, etc. Here, if the designated document 111 is a document in the same field as documents in a field that are frequently included in the comparison document group 100, it is possible to prevent the frequency of appearance of the word 113 that is unlikely to be a typo, etc. from becoming low. This makes it possible to improve the accuracy of detecting typos, etc.

[0102] [Step S14] In step S14, the image generation unit 23 images words 113 that may be typos, i.e., words 113 that appear infrequently in the comparison document set 100, to obtain a verification image 115. For example, words 113 that appear infrequently are below a threshold are imaged. Also, if morphological analysis is performed in step S13, words 113 whose parts of speech could not be determined are imaged.

[0103] When selecting words 113 to be imaged, the variance of the occurrence frequency may be taken into consideration. By taking the variance into consideration, for example, it is possible to determine that a word 113 whose occurrence frequency in the comparison document group 100 is significantly lower than that of other words 113 is likely to be a clerical error, etc. Therefore, it is possible to prevent the proofreading system 10a from determining that a word 113 that is unlikely to be a clerical error, etc. is likely to be a clerical error, etc. Therefore, it is possible for the proofreading system 10a to detect words 113 that are likely to be a clerical error, etc. with high accuracy.

[0104] 5C is a schematic diagram showing an example of the processing in step S14. FIG. 5C shows an example in which the image generating unit 23 images "FET" (F and E are alphabets, and T is a Greek letter) from the word 113 to obtain the verification image 115. As shown in FIG. 5C, the verification image 115 can be binary data in which the text representing the word 113 is white and the background is black, for example.

[0105] [Step S15] In step S15, the similarity obtaining unit 24 compares the verification image 115 with the comparison image 105 included in the comparison image group 104. As a result, the similarity obtaining unit 24 obtains the similarity between the verification image 115 and the comparison image 105. FIG. 5D is a schematic diagram showing an example of the processing in step S15. The verification image 115 represents "FET" (F and E are alphabets, and T is a Greek letter), and has a high similarity with the comparison image 105 representing "FET" (F, E, and T are all alphabets). As described above, the similarity can be obtained by calculation using, for example, region-based matching or feature-based matching.

[0106] [Step S16] In step S16, the presentation unit 14 presents words 103 represented by comparison images 105 with high similarity among the comparison images 105 for which similarity to the verification image 115 was obtained in step S15. The presentation unit 14 preferably presents words 103 represented by comparison images 105 with at least the highest similarity to the verification image 115. For example, the presentation unit 14 can present a predetermined number of words 103, counting from the word 103 represented by the comparison image 105 with the highest similarity to the verification image 115. Alternatively, the presentation unit 14 can present words 103 represented by comparison images 105 with similarity whose difference from the highest similarity is equal to or less than a threshold. Alternatively, the presentation unit 14 can present words 103 represented by comparison images 105 with similarity to the verification image 115 equal to or greater than a threshold.

[0107] 5E is a schematic diagram showing an example of the processing in step S16. As shown in FIG. 5E, presentation unit 14 can be, for example, a display, and can present a message that the word represented by verification image 115 may be a misspelling of word 103 represented by comparison image 105, which has a high similarity.

[0108] Here, the processing unit 13 may have a function of comparing the word 113 represented by the verification image 115 with the word 103 to be presented to the presentation unit 14. This comparison can be performed, for example, by detecting differences between the character code representing the word 113 and the character code representing the word 103 to be presented to the presentation unit 14. This makes it possible to present the differences to the presentation unit 14. FIG. 5E shows an example in which a comment is displayed in the margin of the document to indicate that the "T" in "FET" contained in the document is a Greek letter and may be a misspelling of "FET" (T is an alphabet). The comparison between the word 113 represented by the verification image 115 and the word 103 to be presented to the presentation unit 14 can be performed, for example, by the similarity acquisition unit 24 included in the processing unit 13.

[0109] As described above, the proofreading system 10a can distinguish between characters that look similar but have different character codes. For example, if the designated document 111 contains the word "FET" (F and E are alphabets, and T is a Greek letter), the proofreading system 10a can notify the user that "FET" (F and E are alphabets, and T is a Greek letter) may be a misspelling of "FET" (F, E, and T are all alphabets). Thus, the proofreading system 10a can easily detect errors that are difficult for users to detect visually. Therefore, one aspect of the present invention can provide a proofreading system and a proofreading method that allow users to easily determine whether an error is a misspelling. Furthermore, one aspect of the present invention can provide a highly convenient proofreading system and a proofreading method.

[0110] The proofreading system 10a can also be used to correct characters read by optical character recognition (OCR). For example, suppose a document containing "FET" (F, E, and T are all letters of the alphabet) is read by OCR, but is recognized as "FET" (F and E are letters of the alphabet, and T is a Greek letter). In this case, by designating the document read by OCR as the designated document 111, the proofreading system 10a can correct "FET" (F and E are letters of the alphabet, and T is a Greek letter) to "FET" (F, E, and T are all letters of the alphabet).

[0111] <Proofreading System_2> 6 is a block diagram showing an example configuration of proofreading system 10b. Proofreading system 10b is a modified version of proofreading system 10a, and differs from proofreading system 10a in that processing unit 13 has model calculation unit 25. The following describes proofreading system 10b, focusing on the differences from proofreading system 10a.

[0112] The model calculation unit 25 is supplied with, for example, data output by the division unit 21 and data output by the similarity acquisition unit 24. In addition, the data output by the model calculation unit 25 is supplied to, for example, the presentation unit 14.

[0113] The model calculation unit 25 has a function of performing calculations using a mathematical model. The model calculation unit 25 has a function of performing calculations using, for example, a machine learning model, and has a function of performing calculations using, for example, a neural network model.

[0114] In this specification, a neural network model refers to a general model that mimics the neural network of a living organism, determines the connection strength between neurons through learning, and has problem-solving capabilities. A neural network model has an input layer, an intermediate layer (hidden layer), and an output layer.

[0115] <Proofreading Method_2> An example of a review method using review system 10b will be described below. Data required for review system 10b to have the review function can be obtained, for example, by a method similar to the method shown in Figure 2 and Figures 3A to 3C.

[0116] FIG. 7 is a flowchart showing an example of a proofreading method by the proofreading system 10b, and includes processes from step S11 to step S15 and step S21 to step S23.

[0117] The processing from step S11 to step S15 can be the same as the processing from step S11 to step S15 shown in Fig. 4. In Fig. 7, processing that differs from the processing shown in Fig. 4 is indicated by being surrounded by a dashed line.

[0118] [Step S21] In step S21, the similarity acquisition unit 24 supplies the word 103 represented by the comparison image 105 with the highest similarity among the comparison images 105 for which the similarity with the verification image 115 was acquired in step S15 to the model calculation unit 25. This allows the model calculation unit 25 to acquire the word 103 represented by the comparison image 105 with the highest similarity.

[0119] It is preferable that the similarity acquisition unit 24 supplies at least the word 103 represented by the comparison image 105 having the highest similarity to the verification image 115 to the model calculation unit 25. For example, the similarity acquisition unit 24 can supply a predetermined number of words 103, counting from the word 103 represented by the comparison image 105 having the highest similarity to the verification image 115, to the model calculation unit 25. Alternatively, the similarity acquisition unit 24 can supply to the model calculation unit 25 the word 103 represented by the comparison image 105 whose difference from the highest similarity is equal to or less than a threshold value. Alternatively, the similarity acquisition unit 24 can supply to the model calculation unit 25 the word 103 represented by the comparison image 105 whose similarity to the verification image 115 is equal to or greater than a threshold value.

[0120] [Step S22] In step S22, the model calculation unit 25 obtains, for each word 103, the probability that the word 103 acquired will be replaced with the word 113 corresponding to the verification image 115. Specifically, a language model is incorporated in the model calculation unit 25, and the probability is calculated using the language model. The probability can be calculated based on, for example, a sentence included in the designated document 111. For example, a sentence or a paragraph containing the word 113 corresponding to the verification image 115 is supplied to the language model with the word 113 replaced with the word 103, and the appearance probability of the replaced word 103 is calculated. In this way, the probability that the word 103 acquired by the model calculation unit 25 will be replaced with the word 113 corresponding to the verification image 115 can be calculated.

[0121] The language model may be, for example, a rule-based model. Alternatively, it may be, for example, a model using a conditional random field (CRF). Alternatively, it may be a machine learning model, specifically, for example, a neural network model. As the neural network model, for example, a recurrent neural network (RNN) may be applied. As the architecture of the RNN, for example, a long short-term memory (LSTM) may be used.

[0122] Here, when the model calculation unit 25 calculates the above probability using a machine learning model, it is preferable to use documents that are closely related to the designated document 111 for training the machine learning model, since this allows the above probability to be calculated with high accuracy. As mentioned above, the comparison document group 100 includes, for example, many documents in the same field as the designated document 111. Therefore, it is preferable to use the comparison document group 100 for training the machine learning model.

[0123] [Step S23] In step S23, the presentation unit 14 presents the word 103 with the high probability. It is preferable that the presentation unit 14 presents at least the word 103 with the highest probability. For example, the presentation unit 14 can present a predetermined number of words 103, counting from the word 103 with the highest probability. Alternatively, the presentation unit 14 can present words 103 whose probabilities differ from the highest probability by a threshold or less. Alternatively, the presentation unit 14 can present words 103 whose probabilities are equal to or greater than a threshold.

[0124] In proofreading system 10b, words 103 that appear similar when visualized but have significantly different meanings and are unlikely to be correction candidates for typos or the like in the context can be prevented from being presented to presentation unit 14. Therefore, proofreading system 10b can be a highly convenient proofreading system.

[0125] <Proofreading System_3> 8 is a block diagram showing an example of the configuration of proofreading system 10c. Proofreading system 10c is a modified version of proofreading system 10b, and differs from proofreading system 10b in that processing unit 13 does not have similarity acquisition unit 24. In proofreading system 10c, for example, data output by image generation unit 23 is supplied to model calculation unit 25.

[0126] <Proofreading Method_3> An example of a proofreading method using the proofreading system 10c will be described below. Here, it is assumed that an image judgment model is incorporated into the model calculation unit 25. When data in which a word is visualized is supplied to the model calculation unit 25, the image judgment model has a function of estimating the word represented by the image.

[0127] The image determination model may be, for example, a machine learning model, specifically, a neural network model, for example, a convolutional neural network (CNN).

[0128] The data required for the review system 10c to have the function of performing review can be acquired, for example, by a method similar to the method shown in FIG. 2 and FIGS. 3A to 3C.

[0129] FIG. 9 is a flowchart showing an example of a proofreading method by the proofreading system 10c, and includes processes from step S11 to step S14 and step S31 to step S32.

[0130] The processing from step S11 to step S14 can be the same as the processing from step S11 to step S14 shown in Fig. 4. In Fig. 9, processing that differs from the processing shown in Fig. 4 is indicated by being surrounded by a dashed line.

[0131] [Step S31] In step S31, the verification image 115 is supplied to the image judgment model incorporated in the model calculation unit 25. As a result, the image judgment model estimates the word represented by the verification image 115. Specifically, the image judgment model calculates the probability of the word represented by the verification image 115. For example, when data representing an image of the word "FET" (F and E are alphabets, and T is a Greek letter) is supplied to the image judgment model, the image judgment model can determine that the probability of "FET" (F, E, and T are all alphabets) is high.

[0132] [Step S32] In step S32, the presentation unit 14 presents the estimation result. Specifically, it presents words that are highly likely to be the words represented by the verification image 115. It is preferable that the presentation unit 14 presents at least the words with the highest probabilities. For example, the presentation unit 14 can present a predetermined number of words, counting from the word with the highest probabilities. Alternatively, the presentation unit 14 can present words whose probabilities differ from the highest probabilities by a threshold or less. Alternatively, the presentation unit 14 can present words whose probabilities are equal to or greater than a threshold.

[0133] In the proofreading system 10c, the similarity between the verification image 115 and the comparison image 105 does not need to be calculated by region-based matching, feature-based matching, or the like. This reduces the amount of calculation required in the processing unit 13. This allows the proofreading system 10c to operate at high speed and consume low power.

[0134] [Image judgment model] In the following, an example of the configuration of an image determination model and an example of a learning method when a machine learning model is applied as an image determination model that can be incorporated into the model calculation unit 25 will be described.

[0135] FIG. 10A is a schematic diagram showing an example of a training method for the image judgment model 120. When training the image judgment model 120, first, a training document is supplied to the reception unit 11. Thereafter, the segmentation unit 21 acquires a training term group 122 in a manner similar to step S02 shown in FIG. 2, and the image generation unit 23 acquires a training image group 124 in a manner similar to step S04. The training term group 122 can be a set of words 123, and the training image group 124 can be a set of training images 125. Training of the image judgment model 120 can be performed by supervised learning using data in which the words 123 are associated with the training images 125 as correct labels. Through training, the image judgment model 120 can acquire a training result 126. The training result 126 can be, for example, a weighting coefficient.

[0136] Here, it is preferable to use documents that are closely related to the designated document 111 as the learning documents, since this allows for highly accurate estimation of the words expressed in the verification image 115. As mentioned above, the comparison document group 100 includes many documents in the same field as the designated document 111, for example. Therefore, it is preferable to use the comparison document group 100 as the learning documents.

[0137] Furthermore, the training images 125 included in the training image group 124 are not limited to the images themselves acquired by the image generation unit 23. For example, images obtained by translating, rotating, enlarging, or reducing words included in the images acquired by the image generation unit 23 may be included in the training image group 124. This increases the number of training images 125. Therefore, training can be performed so that the image judgment model 120 can make inferences with high accuracy. Therefore, the proofreading system according to one embodiment of the present invention can detect clerical errors and the like included in the designated document 111 with high accuracy.

[0138] Furthermore, the training image group 124 may include, as the training images 125, images that include characters that look similar but have different character codes. Furthermore, the training image group 124 may include, as the training images 125, images that include, for example, common typographical errors. For example, if the image generating unit 23 visualizes the word "out-of-plane" (- is a hyphen), the training image group 124 may include, in addition to the visualized training image 125, training images 125 that visualize the word "out-of-plane" (- is a minus sign). In this case, the training image 125 that visualizes the word "out-of-plane" (- is a hyphen) and the training image 125 that visualizes the word "out-of-plane" (- is a minus sign) can both be linked to, for example, the word 123 "out-of-plane" (- is a hyphen) as a correct label. Furthermore, for example, if the image generating unit 23 visualizes the word "system," the training image group 124 may include training images 125 obtained by visualizing the word "systm" including a typographical error, in addition to the visualized training images 125. In this case, the training images 125 obtained by visualizing the word "system" and the training images 125 obtained by visualizing the word "systm" can both be associated with the word "system" 123 as a correct label.

[0139] As a result, for example, the verification image 115 supplied to the image judgment model 120 in step S31 shown in FIG. 9 can be made closer to the training image 125. Therefore, the image judgment model 120 can perform inference with high accuracy. Specifically, the word represented by the verification image 115 can be estimated with high accuracy. Therefore, the proofreading system according to one embodiment of the present invention can detect clerical errors and the like contained in the designated document 111 with high accuracy.

[0140] 10B is a schematic diagram showing an example of the configuration and learning method of the image determination model 130. The image determination model 130 has a classifier 131 and a plurality of classifiers 134.

[0141] In this specification and the like, when the same reference numeral is used for multiple elements, and particularly when it is necessary to distinguish between them, a distinguishing symbol such as "_" is added to the reference numeral.

[0142] When an image is supplied to the image determination model 130, the classifier 131 first classifies the image. The image classified by the classifier 131 can be further classified by the classifier 134 corresponding to the result of the classification. Specifically, the classifier 134 can estimate the word represented by the image. That is, after the classifier 131 performs grouping on the image supplied to the image determination model 130, the classifier 134 corresponding to the group to which the image belongs can estimate the word. As described above, the image determination model 130 can perform primary classification by the classifier 131, and then perform secondary classification by the classifier 134.

[0143] FIG. 10B is a schematic diagram showing an example of a learning method for the image determination model 130. FIG. 10B shows an example in which the classifier 131 is learned by clustering, which is unsupervised learning. For example, when a training image group 124 is supplied to the classifier 131, clustering can be performed based on the feature quantities of the training images 125 included in the training image group 124. Clustering can be performed by, for example, the K-means method. Alternatively, clustering can be performed by a single-link method, a complete-link method, a group-average method, Ward's method, a centroid method, a weighted-average method, or a median method. The classifier 131 can obtain a learning result 132 through the above learning. The learning result 132 can be, for example, a weighting coefficient.

[0144] FIG. 10B shows an example in which six images representing the words "a1", "a2", "b1", "FET", "c1", and "c2" are supplied to the classifier 131 as training images 125. FIG. 10B also shows an example in which three clusters 133 are generated by clustering. FIG. 10B also shows an example in which cluster 133_1 includes two training images 125 representing the words "a1" and "a2", cluster 133_2 includes two training images 125 representing the words "b1" and "FET", and cluster 133_3 includes two training images 125 representing the words "c1" and "c2".

[0145] In the example shown in Fig. 10B, a classifier 134 can be provided for each cluster 133. That is, for example, if three clusters 133 are generated by clustering, three classifiers 134 can also be provided. In the example shown in Fig. 10B, an image classified into cluster 133_1 is supplied to classifier 134_1, an image classified into cluster 133_2 is supplied to classifier 134_2, and an image classified into cluster 133_3 is supplied to classifier 134_3.

[0146] The classifier 134 has a function of estimating a word represented by an image. That is, the classifier 134 has the same function as the image determination model 120 shown in FIG. 10A. Furthermore, the classifier 134 can be trained in the same manner as the image determination model 120. That is, the classifier 134 can be trained by supervised learning using data in which the words 123 are associated as correct labels with the training images 125 included in each cluster 133. Through the learning, the classifier 134 can acquire a learning result 135. Here, the learning results 135 acquired by the classifiers 134_1 to 134_3 are referred to as learning result 135_1 to learning result 135_3, respectively. The learning result 135 can be, for example, a weighting coefficient.

[0147] 10B shows an example in which the classifier 131 performs unsupervised learning and the classifier 134 performs supervised learning, but the learning method of the image determination model 130 is not limited to this. For example, both the classifier 131 and the classifier 134 may perform supervised learning.

[0148] The image determination model 130 as a whole can be trained in the same manner as the image determination model 120. That is, for example, by supplying data in which the words 123 are linked to the training images 125 as correct labels to the image determination model 130, the image determination model 130 can be trained by supervised learning.

[0149] For example, when an image such as the verification image 115 is supplied to the image determination model 130 trained by the method shown in Fig. 10B, the image is classified into one of the clusters 133. Then, the word represented by the verification image 115 is estimated by the classifier 134 corresponding to the classified cluster 133.

[0150] In the image determination model 130, after classifying an image into clusters, the word represented by the image is estimated. This allows the size of the classifier 134, which is a model for estimating the word represented by the image, to be reduced. Therefore, the image determination model 130 is a machine learning model that is easy to learn and can perform inference with high accuracy. Specifically, the word represented by the verification image 115 can be estimated with high accuracy. Therefore, the proofreading system of one embodiment of the present invention can detect errors and the like contained in the designated document 111 with high accuracy. Note that while FIG. 10B shows an example in which the image determination model 130 performs up to second-order classification, it may also perform up to third-order classification, or even fourth-order classification or more. For example, if the image determination model 130 performs up to third-order classification, the word represented by the image can be estimated by the third-order classification.

[0151] <Proofreading Method_4> The above-described reviewing methods 1 to 3 can be combined as appropriate. FIG. 11 is a flowchart showing an example of a reviewing method that combines the methods shown in reviewing methods 1 to 3, and includes processing from steps S11 to S15 and steps S41 to S43. The processing shown in FIG. 11 can be performed by the reviewing system 10b. Here, the model calculation unit 25 is assumed to incorporate an image determination model in addition to a language model.

[0152] The processing from step S11 to step S15 can be the same as the processing from step S11 to step S15 shown in Fig. 4. In Fig. 11, processing that differs from the processing shown in Fig. 4 is indicated by being surrounded by a dashed line.

[0153] [Step S41] In step S41, the verification image 115 is supplied to the image judgment model incorporated in the model calculation unit 25. As a result, the model calculation unit 25 calculates the probability of the word represented by the verification image 115. This probability is set as the first probability. The first probability is calculated taking into consideration the similarity acquired by the similarity acquisition unit 24 in step S15. For example, the first probability is calculated by adding a value corresponding to the similarity between the comparison image 105, which is an image of the word for which the probability was calculated, and the verification image 115, to a value corresponding to the probability calculated by the image judgment model. Step S41 allows the model calculation unit 25 to acquire the first probability.

[0154] [Step S42] In step S42, the model calculation unit 25 obtains the probability that the word with the highest first probability will be replaced as the word 113 corresponding to the verification image 115. This probability is defined as the second probability. The second probability can be calculated using a language model incorporated in the model calculation unit 25.

[0155] Here, it is preferable that the model calculation unit 25 calculates the second probability for at least the word with the highest first probability. For example, the model calculation unit 25 can calculate the second probability for a predetermined number of words, starting from the word with the highest first probability. Alternatively, the model calculation unit 25 can calculate the second probability for words whose first probabilities differ from the highest first probability by a threshold or less. Alternatively, the model calculation unit 25 can calculate the second probability for words whose first probabilities are equal to or greater than a threshold.

[0156] [Step S43] In step S43, the presentation unit 14 presents words with high second probabilities. It is preferable that the presentation unit 14 presents at least the words with the highest second probabilities. For example, the presentation unit 14 can present a predetermined number of words counting from the word with the highest second probability. Alternatively, the presentation unit 14 can present words with second probabilities whose difference from the highest second probability is equal to or less than a threshold value. Alternatively, the presentation unit 14 can present words with second probabilities equal to or greater than a threshold value.

[0157] For example, by operating a proofreading system according to one embodiment of the present invention using the method shown in FIG. 11, it is possible to improve the accuracy of detecting typographical errors and the like contained in the designated document 111 while also improving the convenience of the proofreading system according to one embodiment of the present invention.

[0158] <Proofreading Method_5> FIG. 12 is a flowchart showing an example of a proofreading method by the proofreading system 10b, and includes processes from step S11 to step S15, step S21 to step S22, and step S51 to step S53.

[0159] The processes from step S11 to step S15 and step S21 to step S22 can be the same as the processes shown in Fig. 7. In Fig. 12, processes that differ from the processes shown in Fig. 7 are indicated by being surrounded by dashed lines.

[0160] [Step S51] In step S51, the model calculation unit 25 acquires homonyms of words 103 with high probabilities among the words 103 for which the probability of being replaced as the word 113 corresponding to the verification image 115 has been acquired. It is preferable that the model calculation unit 25 acquires homonyms of at least the word 103 with the highest probability. For example, the model calculation unit 25 can acquire homonyms of a predetermined number of words 103, counting from the word 103 with the highest probability. Alternatively, the model calculation unit 25 can acquire homonyms of words 103 with probabilities whose difference from the highest probability is equal to or less than a threshold value. Alternatively, the model calculation unit 25 can acquire homonyms of words 103 with probabilities equal to or greater than a threshold value.

[0161] [Step S52] In step S52, the model calculation unit 25 obtains the probability that the obtained homonym will be replaced as the word 113 corresponding to the verification image 115. The probability can be calculated using a language model incorporated in the model calculation unit 25.

[0162] [Step S53] In step S53, the model calculation unit 25 presents the word 103 itself from which the homonyms have been obtained, and homonyms whose probability of being replaced as the word 113 corresponding to the verification image 115 is increased compared to the word 103, to the presentation unit 14. For example, homonyms whose probability is increased by a threshold or more compared to the probability of the word 103 can be presented to the presentation unit 14.

[0163] By operating the proofreading system 10b using the method shown in FIG. 11, the proofreading system 10b can detect typographical errors caused by homonyms. For example, if the designated document 111 contains Japanese text, it can detect incorrect conversion of kanji characters. This improves the convenience of the proofreading system 10b.

[0164] <Proofreading Method_6> In the methods shown in FIGS. 4, 7, 9, 11, and 12, in step S12, the segmentation unit 21 segments a sentence included in the designated document 111 into words 113. As described above, for example, an English sentence can be segmented into words 113 based on spaces. In this case, if the designated document 111 contains, for example, the word “transistor” as a misspelling of “transistor,” “tran” and “sistor” may be segmented as different words 113. If the word “tran” is not included in the comparison term group 102, there may be no comparison image 105 that is highly similar to the verification image 115 that is an image of the word “tran.” Similarly, if the word “sistor” is not included in the comparison term group 102, there may be no comparison image 105 that is highly similar to the verification image 115 that is an image of the word “sistor.” Therefore, even if the designated document 111 contains, for example, a word “tran sistor,” “transistor” may not be presented as a correction candidate.

[0165] In such a case, it is preferable to divide the sentence into a predetermined number of characters using N-gram (also called N-character indexing or N-gram method, etc.) etc. For example, when dividing the sentence contained in the designated document 111 into 10 characters, if spaces are not included in the number of characters, "trans sistor" can be made into one word 113.

[0166] Specifically, for example, in step S12, a sentence contained in the designated document 111 is divided into words 113 based on spaces. Therefore, if the designated document 111 contains the word "tran sistor," "tran" and "sistor" are divided into different words 113 in step S12.

[0167] In step S13, the occurrence frequency acquisition unit 22 acquires the occurrence frequency of the word 113 in the comparison document set 100. Here, it is assumed that the occurrence frequency of "tran" and the occurrence frequency of "sistor" are both low. It is also assumed that the occurrence frequency of the word 113 immediately before "tran" and the occurrence frequency of the word 113 immediately after "sistor" are both high. In this case, N-gram is applied to a series of words 113 with low occurrence frequency sandwiched between words 113 with high occurrence frequency. It is assumed that the occurrence frequency acquisition unit 22 has thereby acquired the word 113 "tran sistor".

[0168] In step S14, the image generation unit 23 generates images of the words 113 that appear less frequently in the comparison document set 100 as well as the words 113 obtained by N-gram, to obtain a verification image 115. Thereafter, the processing shown in FIG. 4, FIG. 7, FIG. 9, FIG. 11, or FIG. 12 is performed.

[0169] Verification image 115, which is an image of word "transistor" 113, has a high similarity to comparison image 105, which is an image of word "transistor" 103. Therefore, presentation unit 14 can present the possibility that "transistor" contained in designated document 111 is a misspelling of "transistor." This can improve the convenience of the proofreading system according to one embodiment of the present invention.

[0170] FIG. 13 is a conceptual diagram showing the proofreading system of this embodiment.

[0171] 13 includes a server 1100 and terminals (also called electronic devices). Communication between the server 1100 and each terminal can be performed via an internet line 1110.

[0172] The server 1100 can perform calculations using data input from a terminal via an internet line 1110. The server 1100 can transmit the results of the calculations to the terminal via the internet line 1110. This can reduce the calculation load on the terminal.

[0173] FIG. 13 shows information terminal 1300, information terminal 1400, and information terminal 1500 as terminals. Information terminal 1300 is an example of a mobile information terminal such as a smartphone. Information terminal 1400 is an example of a tablet terminal. Furthermore, information terminal 1400 can also be used as a notebook information terminal by connecting it to a housing 1450 having a keyboard. Information terminal 1500 is an example of a desktop information terminal.

[0174] By configuring the server 1100 in this manner, users can access the server 1100 from the information terminal 1300, the information terminal 1400, the information terminal 1500, and the like. The users can then receive services provided by the administrator of the server 1100 through communication via the Internet line 1110. For example, such services may include services using the proofreading system according to one embodiment of the present invention. In such services, the server 1100 may use artificial intelligence. [Explanation of symbols]

[0175] 10a: Review system, 10b: Review system, 10c: Review system, 11: Reception unit, 12: Memory unit, 13: Processing unit, 14: Presentation unit, 21: Segmentation unit, 22: Occurrence frequency acquisition unit, 23: Image generation unit, 24: Similarity acquisition unit, 25: Model calculation unit, 100: Comparison document group, 101: Comparison document, 102: Comparison term group, 103: Word, 104: Comparison image group, 105: Comparison image, 111: Designated document, 112: Designated document term group, 1 13: Word, 115: Verification image, 120: Image judgment model, 122: Learning term group, 123: Word, 124: Learning image group, 125: Learning image, 126: Learning result, 130: Image judgment model, 131: Classifier, 132: Learning result, 133: Cluster, 134: Classifier, 135: Learning result, 1100: Server, 1110: Internet line, 1300: Information terminal, 1400: Information terminal, 1450: Housing, 1500: Information terminal

Claims

1. The image processing apparatus includes a dividing unit, an appearance frequency acquiring unit, an image generating unit, a model calculating unit, and a presenting unit, the dividing unit has a function of dividing a sentence included in a comparison document group into a plurality of first words and a function of dividing a sentence included in a designated document into a plurality of second words; the occurrence frequency acquisition unit has a function of acquiring occurrence frequencies of the plurality of second words in the comparison document group, the image generation unit has a function of generating images of the first words to obtain a group of comparison images; the image generating unit has a function of generating an image of the second word, the appearance frequency of which is equal to or less than a first threshold, from among the plurality of second words to obtain a verification image; the model calculation unit has a function of estimating a word represented by the verification image, The presentation unit is a proofreading system having a function of presenting the results of the estimation.

2. In claim 1, The model calculation unit is a proofreading system having the function of performing calculations using a machine learning model.

3. In claim 2, A proofreading system in which the machine learning model is trained using the group of comparison images.

4. In claim 3, A proofreading system in which the machine learning model is trained through supervised learning using data in which words are linked as correct labels to comparison images included in the group of comparison images.

5. In claim 3 or 4, the machine learning model includes a first classifier and two or more second classifiers; the first classifier has a function of performing grouping on the comparison images included in the comparison image group; the second classifier has a function of estimating words represented by the grouped comparison images, A proofreading system in which the word represented by the comparison image is estimated using the second classifier, which differs for each group.

6. In any one of claims 2 to 5, The machine learning model is a neural network model.

Images