WO2020071558A1 - 帳票レイアウト解析装置、その解析プログラムおよびその解析方法 - Google Patents

帳票レイアウト解析装置、その解析プログラムおよびその解析方法

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Publication number
WO2020071558A1
WO2020071558A1 PCT/JP2019/039412 JP2019039412W WO2020071558A1 WO 2020071558 A1 WO2020071558 A1 WO 2020071558A1 JP 2019039412 W JP2019039412 W JP 2019039412W WO 2020071558 A1 WO2020071558 A1 WO 2020071558A1
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WO
WIPO (PCT)
Prior art keywords
item
layout
area
attribute
image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2019/039412
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English (en)
French (fr)
Japanese (ja)
Inventor
諒介 佐々木
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Arithmer Inc
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Arithmer Inc
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Publication date
Application filed by Arithmer Inc filed Critical Arithmer Inc
Priority to JP2020551133A priority Critical patent/JP7396568B2/ja
Publication of WO2020071558A1 publication Critical patent/WO2020071558A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition

Definitions

  • the present invention relates to a form layout analyzing apparatus for analyzing a form layout, a program for analyzing the same, and a method for analyzing the same.
  • Patent Document 1 discloses a document editing and output device that analyzes a document structure in a document image using a template that defines the layout of the document.
  • Patent Literature 2 it is possible to easily impose more detailed OCR constraints by enabling the data type of a field to be semi-automatically set by a learning function, thereby improving the accuracy of character recognition.
  • An information processing device is disclosed. Specifically, the format information and the constraint corresponding to the input form image are read from the format model storage unit, and the entry value of the form image in the field specified by the format information is within the range of the field constraint. Is recognized.
  • Patent Literature 1 and Patent Literature 2 are based on the premise that templates and formats of forms are registered in the system in advance, and therefore cannot deal with unknown forms not registered in the system.
  • an object of the present invention is to enable layout analysis of an unknown form not registered in the system.
  • a first invention provides a form layout analyzing apparatus that includes an item extracting unit and a layout analyzing unit and analyzes a form layout.
  • the item extraction unit uses the object detection algorithm based on deep learning to individually extract the item areas included in the form image with the attribute classification, using the object detection algorithm based on deep learning for the item areas including the item names printed in print on the form. Extract.
  • the layout analysis unit analyzes the layout of the form image based on the position of the item area on the form image and its attribute.
  • a handwritten region extracting unit for individually extracting a handwritten character region including a character string written with handwritten characters on the form from the form image may be further provided.
  • the layout analysis unit assigns one of the attributes classified by the item extraction unit to each of the handwritten character regions extracted by the handwriting region extraction unit.
  • the layout analysis unit associates the item area with a handwritten character area located in the vicinity thereof in accordance with a preset correspondence rule, and then associates the item area with the item associated with the handwritten character area.
  • Area attributes may be assigned.
  • the item extraction unit inputs the form image to one neural network as an object detection algorithm, and collects the extraction of the item region and the classification of the attribute by a regression problem approach. It is preferred to do so.
  • the item extraction unit refers to a learning model constructed by supervised learning using teacher data which is a pair of an item image including an item name represented in print and an attribute of the item image, The extraction of the item area and the classification of the attribute may be performed.
  • the item extracting unit may output the classification accuracy of the attribute.
  • the layout analysis unit may present the plurality of analysis results to the user as layout candidates.
  • the second invention provides a form layout analysis program for causing a computer to execute processing having the following steps and analyzing the form layout.
  • item regions including item names printed in type on the form are extracted, and the object regions included in the form image are individually classified with attribute classification using an object detection algorithm by deep learning.
  • attribute classification using an object detection algorithm by deep learning.
  • the layout of the form image is analyzed based on the position of the item area on the form image and its attribute.
  • a third step of individually extracting a handwritten character area including a character string written with handwritten characters on the form from the form image may be further provided.
  • the item area is associated with a handwritten character area located in the vicinity thereof according to a preset correspondence rule, and the item area is associated with the handwritten character area.
  • the attribute of the item area may be assigned.
  • the first step is to input a form image into one neural network as an object detection algorithm, and to extract an item area and classify its attributes by a regression problem approach. It is preferable to carry out.
  • the first step refers to a learning model constructed by supervised learning using teacher data, which is a pair of an item image including an item name represented in print and an attribute of the item image.
  • teacher data which is a pair of an item image including an item name represented in print and an attribute of the item image.
  • the extraction of the item area and the classification of its attribute may be performed.
  • the first step may include a step of outputting the classification accuracy of the attribute.
  • the second step may include a step of presenting the plurality of analysis results to the user as layout candidates when a plurality of analysis results are obtained for the layout of the form image.
  • the third invention provides a form layout analysis method having the following steps and analyzing a form layout.
  • item regions including item names printed in type on the form are extracted, and the object regions included in the form image are individually classified with attribute classification using an object detection algorithm by deep learning.
  • attribute classification using an object detection algorithm by deep learning.
  • the layout of the form image is analyzed based on the position of the item area on the form image and its attribute.
  • a third step of individually extracting a handwritten character area including a character string written with handwritten characters on the form from the form image may be further provided.
  • the second step it is preferable to assign one of the attributes classified in the first step to each of the handwritten character regions extracted in the third step.
  • the item area is associated with a handwritten character area located in the vicinity thereof according to a preset correspondence rule, and the item area is associated with the handwritten character area.
  • the attribute of the item area may be assigned.
  • the first step is to input a form image to one neural network as an object detection algorithm, and to extract an item area and classify its attributes by a regression problem approach. It is preferable to carry out.
  • the first step refers to a learning model constructed by supervised learning using teacher data, which is a pair of an item image including an item name represented in print and an attribute of the item image.
  • teacher data which is a pair of an item image including an item name represented in print and an attribute of the item image.
  • the extraction of the item area and the classification of its attribute may be performed.
  • the first step may include a step of outputting the classification accuracy of the attribute.
  • the second step may include a step of, when a plurality of analysis results are obtained for the layout of the form image, presenting the plurality of analysis results to the user as layout candidates.
  • the item region included in the form image and the attribute thereof are acquired using the object detection algorithm based on deep learning. From these pieces of information, it is possible to identify what information is described in which position in the form image. This makes it possible to perform a layout analysis even for an unknown form not registered in the system.
  • Block diagram of a form layout analysis device Illustration of the object detection algorithm YOLO network configuration diagram
  • Layout analysis flowchart Diagram showing an example of a form image Diagram showing item areas extracted from form images
  • Diagram showing handwritten character areas extracted from form images
  • Diagram showing layout analysis result of form image Explanatory diagram of layout candidates by extracting multiple attributes Explanatory drawing of layout candidates due to proximity of a plurality of item areas
  • FIG. 1 is a block diagram of a form layout analysis device according to the present embodiment.
  • the form layout analyzing apparatus 1 analyzes the layout of a form in which a handwritten character string such as an application form or a contract is entered, and specifies what is described in the form.
  • the form to be analyzed is an unknown form, that is, a form whose layout is not registered in the system, and is performed, for example, as preprocessing prior to optical character recognition (OCR) of a handwritten form.
  • the form layout analysis apparatus 1 mainly includes an item extraction unit 2, a handwritten region extraction unit 3, a filter processing unit 4, a layout analysis unit 5, a learning processing unit 6, a learning model 7, and a correspondence rule table 8. Is configured.
  • the item extracting unit 2 individually extracts the item regions included in the form image with the attribute classification, with the item regions including the item names printed in print on the form as extraction targets. For example, if there is an image area such as “name” or “address” in the form image, each image area is extracted as an item area, and “name” or “address” is assigned to each item area. Attributes are added.
  • the extraction of the item area is performed using an object detection algorithm based on deep learning, and the extraction of the item area and the classification of its attributes are performed with reference to the learning model 7 constructed based on this algorithm. For the classified attributes, the classification accuracy is also calculated and output.
  • FIG. 2 is an explanatory diagram of the object detection algorithm.
  • processing for an input is divided into three stages: area search, feature extraction, and machine learning. That is, an area search is performed, features are extracted in accordance with an object to be detected, and an appropriate machine learning method is selected.
  • the object detection is realized by being divided into three algorithms.
  • the feature amount basically, only a specific target can be detected because it is designed exclusively for the detection target. Therefore, in order to eliminate such a restriction, an object detection algorithm based on deep learning as shown in FIGS. As shown in FIG.
  • the processing is completed in one network. After data input, it can be said that it is an “End-to-End” process in that it goes to the end (output result) only by deep learning.
  • the present embodiment is characterized in that items on a form are extracted using an object detection algorithm based on deep learning, and in particular, employs a method shown in FIG. 3C typified by YOLO or SSD.
  • the YOLO process is generally as follows. First, the input image is divided into S * S areas. Next, the class probabilities of the objects in each region are derived. Then, the parameters (x, y, height, width) and the reliability (confidence) of the B (hyperparameter) bounding boxes are calculated. The bounding box is a circumscribed rectangle of the object area, and the reliability is the degree of coincidence between the prediction and the correct bounding box. For the object detection, the product of the class probability of the object and the reliability of each bounding box is used.
  • FIG. 3 is a network configuration diagram of YOLO.
  • a form image is input to a CNN (Convolutional Neural Network) layer, and the result is output through a plurality of fully connected layers.
  • the output includes the image area divided into S * S pieces, five parameters of a bounding box (BB) including the reliability (classification accuracy), and the number of classes (attributes of items).
  • BB bounding box
  • the handwritten region extraction unit 3 individually extracts a handwritten character region including a character string written with handwritten characters on the form from the form image.
  • Various methods have been proposed for discriminating between handwritten characters and printed characters, and any method can be used.
  • a method of analyzing a character image in a real space may be used. Specifically, a character string is extracted by taking a histogram of the character in the horizontal direction and the vertical direction, and a handwritten character string is extracted by evaluating the linearity of the base line.
  • the variation in the size of each handwritten character constituting the character string, the degree of proximity between the handwritten characters, and the like may be considered.
  • a method of analyzing the character image in the frequency space may be used.
  • the handwritten region extraction unit 3 may use a model for identifying “printed / printed”, “handwritten”, “ruled line”, “chiji”, and “background” in pixel units.
  • a method such as Semantic Segmentation may be used as a classifier.
  • the filter processing unit 4 regards, as a noise, an attribute whose reliability (classification accuracy) is smaller than a predetermined threshold value among a plurality of attributes extracted by the item extraction unit 2. Information about the item area filtered by the filter processing unit 4 is output to the layout analysis unit 5.
  • the layout analysis unit 5 analyzes the layout of the form image based on the position of the item area on the form image and its attribute, and specifies which attribute information is entered and where. Specifically, one of the attributes classified by the item extracting unit 2 is assigned to each of the handwritten character regions extracted by the handwritten region extracting unit 3. Basically, when a certain item area and a certain handwritten character area are close to each other on the form image, that is, when the distance between the two is less than or equal to a predetermined threshold value, the two are associated with each other, Is assigned the attribute of this item area. For example, when a handwritten character area exists near an item area having an attribute of "name", an attribute of "name” is assigned to this handwritten character area.
  • correspondence rule table 8 a specific correspondence rule between the item area and the handwritten character area is set and defined in advance in the correspondence rule table 8.
  • This correspondence rule is, in addition to the basic rule of associating the two when a handwritten character area exists near the right of the item area, or associating the two when a handwritten character area exists near the bottom of the item area. It also defines the handling of handwritten character areas that exist in the table.
  • the learning processing unit 6 constructs the learning model 7 by supervised learning using teacher data which is a pair of an item image (partial image) including an item name represented in print and an attribute of the item image.
  • teacher data which is a pair of an item image (partial image) including an item name represented in print and an attribute of the item image.
  • the learning model 7 referred to by the item extraction unit 2 can be reconstructed ex post facto with an increase in teacher data.
  • FIG. 4 is a flowchart of a layout analysis performed by the form layout analysis device 1.
  • the form layout analysis apparatus 1 can be equivalently realized by installing a computer program (form layout analysis program) for causing a computer to function and operate as the blocks 2 to 6 in the computer.
  • FIG. 5 is a diagram illustrating a “transfer request form” as an example of a form image.
  • item names such as “name”, “affiliation”, “amount”, and “bank name” are printed on the form in print.
  • items corresponding to the item names are written by hand.
  • the item extracting unit 2 individually extracts the item areas present in the form image with attributes.
  • a rectangular area including the print string “reading” is extracted as the item area a1, and the attribute “phonetic” and the classification probability are added.
  • the print string “affiliation” a rectangular area including this is extracted as the item area a2, and the attribute “department” and the classification probability are given.
  • a rectangular area including the print string "name” is extracted as the item area a3, and the attribute "name” and the classification probability are given.
  • the print string "money” a rectangular area including the same is extracted as the item area a4, and the attribute "amount” and the classification probability are given.
  • a rectangular region including this is extracted as the item region a5, and the attribute “bank” and the classification probability are given.
  • a rectangular area including the print string “branch name” is extracted as the item area a6, and the attribute “branch” and the classification probability are given.
  • the print sequence “deposit type” a rectangular area including the same is extracted as the item area a7, and the attribute “account @ type” and the classification probability are given.
  • the print string “account number” a rectangular area including the print string is extracted as the item area a8, and the attribute “account @ number” and the classification probability are given.
  • the handwritten region extracting unit 4 individually extracts the handwritten character regions present in the form image.
  • a rectangular area including the handwritten character string “Tokyo Ichiro” is extracted as a handwritten character area b1.
  • the handwritten character string “Intellectual Property Department” a rectangular area including the same is extracted as the handwritten character area b2.
  • the handwritten character string “Patent No. Ichiro” a rectangular area including this is extracted as the handwritten character area b3.
  • the handwritten character string “6,500” a rectangular area including the character string is extracted as the handwritten character area b4.
  • a rectangular area including this is extracted as the handwritten character area b5.
  • a rectangular area including the character string is extracted as a handwritten character area b6.
  • a rectangular area including this is extracted as a handwritten character area b7.
  • a rectangular area including the same is extracted as the handwritten character area b8.
  • step 4 the filtering unit 4 filters the attributes extracted by the item extraction unit 2, and removes the attributes regarded as noise.
  • the layout analysis section 5 analyzes the layout of the form image.
  • the handwritten character area b1 is located near the right of the item area a1, and thus the attribute “phonetic” of the item area a1 is assigned. Since the handwritten character area b2 is located near the right of the item area a2, the attribute "department” of the item area a2 is assigned. Since the handwritten character area b3 is located near the right of the item area a3, the attribute "name" of the item area a3 is assigned. Since the handwritten character area b4 is located near the right of the item area a4, the attribute "amount" of the item area a4 is assigned.
  • the attribute "bank” of the item area a5 is assigned.
  • the attribute “branch” of the item area a6 is assigned to the handwritten character area b6 because it is located below and below the item area a6. Since the handwritten character area b7 is located below and below the item area a7, the attribute "account @ type" of the item area a7 is assigned. Since the handwritten character area b8 is located near the lower part of the item area a8, the attribute “account @ number” of the item area a8 is assigned.
  • step 6 the analysis result of the layout of the form image as shown in FIG. 8 is output, and a series of processing ends.
  • Step 2 and Step 3 are independent of each other, their execution order may be reversed, or they may be executed concurrently.
  • a plurality of analysis results are obtained by the layout analysis in step 5, these analysis results may be presented to the user as layout candidates in order to leave the eligibility to the user's judgment.
  • the following two cases can be considered as such a case.
  • a plurality of attributes 1 and 2 are assigned to one item area a on a form image.
  • both attribute 1 and attribute 2 can be considered as attributes of the handwritten region b located near the item region a
  • both attributes 1 and 2 are considered as candidates for the attribute of the handwritten character region b.
  • a plurality of item areas a1 and a2 are close to a certain handwritten character area b.
  • both the attribute 3 of the item area a1 and the attribute 4 of the item area a2 can be considered as the attributes of the handwritten area b
  • both the attributes 3 and 4 are provided to the user as candidates for the attributes of the handwritten character area b. Be presented.
  • the item region included in the form image and the attribute thereof are acquired using the object detection algorithm based on deep learning. From these pieces of information, it is possible to identify what information is described in which position in the form image. This makes it possible to perform a layout analysis even for an unknown form not registered in the system.
  • an object detection algorithm based on deep learning various methods such as YOLO and SSD, which collectively extract an item area in a form image and classify its attributes by one neural network, are used. The object can be detected at high speed.
  • a handwritten character area including a character string written with handwritten characters on a form is individually extracted from the form image, and each of the handwritten character areas is classified by the item extracting unit 2. Assign one of the specified attributes. This makes it possible to perform a layout analysis even on a form in which a handwritten character string is entered.
  • REFERENCE SIGNS LIST 1 form layout analysis device 2 item extraction unit 3 handwritten region extraction unit 4 filter processing unit 5 layout analysis unit 6 learning processing unit 7 learning model 8 correspondence rule table

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Character Input (AREA)
  • Image Analysis (AREA)
PCT/JP2019/039412 2018-10-05 2019-10-04 帳票レイアウト解析装置、その解析プログラムおよびその解析方法 Ceased WO2020071558A1 (ja)

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JP2021179747A (ja) * 2020-05-12 2021-11-18 京セラドキュメントソリューションズ株式会社 帳票データ取得システムおよび帳票データ取得プログラム
JP2021197154A (ja) * 2020-06-09 2021-12-27 ペキン バイドゥ ネットコム サイエンス アンド テクノロジー カンパニー リミテッドBeijing Baidu Netcom Science And Technology Co., Ltd. 帳票画像認識方法および装置、電子機器、記憶媒体並びにコンピュータプログラム
JP2022029228A (ja) * 2020-08-04 2022-02-17 キヤノン株式会社 画像処理装置、画像形成システム、画像処理方法、およびプログラム
JP2022032831A (ja) * 2020-08-14 2022-02-25 株式会社インフォディオ 情報処理装置及びプログラム
JP7452809B1 (ja) * 2023-08-09 2024-03-19 ファーストアカウンティング株式会社 情報処理装置、情報処理方法及びプログラム

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JP7570843B2 (ja) 2020-08-04 2024-10-22 キヤノン株式会社 画像処理装置、画像形成システム、画像処理方法、およびプログラム
JP2022032831A (ja) * 2020-08-14 2022-02-25 株式会社インフォディオ 情報処理装置及びプログラム
JP7452809B1 (ja) * 2023-08-09 2024-03-19 ファーストアカウンティング株式会社 情報処理装置、情報処理方法及びプログラム
WO2025032762A1 (ja) * 2023-08-09 2025-02-13 ファーストアカウンティング株式会社 情報処理装置、情報処理方法及びプログラム

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