JP2005044086A - Character recognition method and device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an item reading means for allowing the arbitrary combination of a character string frame and a selection branches frame and a highly precise item reading means corresponding to the various entry methods of a person who fills in a business form. <P>SOLUTION: Item reading which permits the arbitrary combination of a character string frame and a selection branches frame is executed by a recognition processing following a business form definition dictionary using a frame structural format for absorbing the difference of the character string frame and the selection branches frame and its frame structural format. Also, the recognition result of each character string frame or selection branches frame is expressed with the format of a character segmentation graph, and the recognition results of those respective frames are integrated by connecting them, and character string collation is executed to the finally acquired character segmentation graph so that the highly precise item reading can be executed. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

と連接すれば、表３１０に記入された文字列を得ることができる。ちなみに記号「×」は連接を表す記号とする。数１のような、読取り対象の枠構造を表現する式を、以降「枠構造式」とよぶ。
【００１３】
しかし、選択肢においてはアクセプトの条件として、選択の最大・最小数が規定されることがある。たとえば図３において、都道府県の選択肢３３０は、マークが最大１つでないと、地名として意味をなさない。また選択肢の前後の文字列枠に、都道府県の文字を記入される可能性があるため、選択肢の最小数は０である。したがって最大数１、最小数０を指定する必要がある。
そのような場合を考慮して枠構造式を拡張する。すなわち、連接のみでなく、選択を考慮し、選択記号＋を新たに定義する。また選択肢の集合を括弧（）で括り、末尾に大括弧［］で最小、最大数を記入することとする。すなわち数１は

と拡張される。これにより、選択肢の選択数までも考慮した枠構造式を定義できる。
【００１４】
本発明は、まず第１の課題を解決するために、文字列枠と選択肢枠の差異を吸収する前記枠構造式を用いた帳票定義辞書とその枠構造式に沿った認識処理によって、文字列枠と選択肢枠との任意の組合せを許容する項目読取りを可能とする。
【００１５】
また、第２の課題を解決するために、各文字列枠や選択肢枠の認識結果を文字切出しグラフの形式で表現し、それらを結合させることにより各枠の認識結果を統合させ、最終的に得られた文字切出しグラフに対し文字列照合を行うことにより、高精度な項目読取りを可能とする。
【００１６】
【発明の実施の形態】
本発明の実施形態にかかる文字認識装置の構成について説明する（図１）。文字認識装置１００は、記入された文字や図形などを電子的に入力する画像入力部１０１、入力された記入画像から指定された箇所を読取る文字認識部１０２、その認識結果を出力する認識結果出力部１０３を有す。また、読取対象の帳票のレイアウトや各記入枠の属性（枠内にどのような文字列が記載されるかなど）等の情報が記述された帳票定義辞書１０４、読取り対象の文字列の表記知識が格納され文字列照合で参照される文字列表記辞書１０５を有す。文字列表記辞書の構造として、（１）認識対象文字列の羅列、（２）認識対象文字列を受理するオートマトン、（３）認識対象文字列を記述する文脈自由文法、などが利用可能である。
【００１７】
画像入力を実現する手段としては、記入されるメディアの形態などにより種々の手段が利用可能である。たとえば、紙面に記入する場合は光学的スキャナにより紙面イメージを取り込む手段や紙下にタブレットを置き記入する手段があり、また液晶画面に記入する場合にはタッチパネルにより記入された筆跡を得る手段もある。さらに近年では、紙面上に記入するとその筆跡を測定できる電子ペンを用いる手段もある。その電子ペンの一例として、特許文献３で開示されているものについて簡単に説明する（図２）。２００が電子ペンであり、２０１は紙上に印刷された位置検出用の模様の画像を取得するカメラデバイスである。紙２１０を拡大してみると（２１１）、小さなドットが非均質に散りばめられて印刷されている。このドットは仮想的な格子線２１２における交点からある距離だけ上下左右の何れかにずらされて印刷されており、このドットを同時に複数個、例えば６×６の範囲で参照し、これら３６個のドットの上下左右のずれの値の組み合わせが、広大な平面領域の絶対位置情報を与える仕組みとなっている。つまり電子ペンに備え付けられたカメラ２０１により上記複数のドットパタンを含む範囲の画像を一定の時間間隔で採取することにより、上記ずれの組み合わせの空間上での位置を特定することが可能となる。本原理の詳細は特許文献４で明らかにされている。
【００１８】
電子ペンや、タブレット、タッチパネルなどペン軌跡を記録する電子化手段を用いた場合の記入情報は、図８のようなデータ構造となる。記入情報８１０は、そのなかに含まれる総ストローク数８１１、各ストロークの標本点数８１２、標本点へのポインタ８１３、を有する。標本点へのポインタは、標本点情報８２０の１標本８２１を指しており、その位置から標本点数で指定された個数までがそのストロークを表す標本点配列となる。電子ペンは、このように記入ストロークの動的情報を記憶できる他、罫線等を記憶せずにペン記入情報を記憶することができ、また、スキャニングのように掠れたり潰れたりすることなくペン軌跡を記憶することができるという利点がある。ここでは、前記電子ペンを利用した形態で以降説明する。
【００１９】
次に帳票定義辞書１０４の構造について具体例をあげて説明する。前述のようにたとえば図３に示した枠構造における枠構造式は、セル３２１，３２２，３２３，３３１，３３２，３３３，３３４，３４０，３５０を、おのおのセルＡ‥Ｉと置くと、数２より、
Ａ×Ｂ×Ｃ×（Ｄ＋Ｅ＋Ｆ＋Ｇ）［０，１］×Ｈ×Ｉ （数３）
とおける。この式のまま帳票定義とし計算機上に実装したのでは、処理時に参照する際、構文解析などの手段が必要となり、実際問題として煩雑である。そこで本実施例では、枠構造式を、図４に示したような木構造の形式で表現することとする。これは各セルに関し、文字列枠の部分と選択肢の部分との部分集合に分割することにより木構造とするものである。以降、枠構造式から作成される木を「枠構造木」、セルの部分集合のことを「セルバインダ」とよぶことにする。たとえば前記の数３においては、

に変形することにより、図４に示した枠構造木４１０を得ることができる。
【００２０】
なお、図４に示したような枠構成木は、選択肢セルと文字列セルの両タイプの部分集合においてセルバインダを導入するため、木の階層が常に３階層と平衡木になる。セルバインダを導入せずに各セルを直接、最上位の節である読取項目４１１と接続させてもよい。しかしその場合、最上位節の読取項目４１１からみて全てのセルが自分の子となるため、認識時に子が選択肢セルか文字列セルかで分岐処理をしなければならなる。したがってセルバインダを一律に導入して３階層の平衡木とした方が、（１）木構造を表現するデータ構造体が定義しやすい、（２）認識処理フローがより単純となる、といった利点があるため本実施例ではセルバインダを導入した枠構造木を採用することとする。
【００２１】
さらに、枠構造木４１０のデータ構造を図５に示す。まず読取項目４１１に該当するデータ構造が読取項目情報５００である。このデータ構造は、読取項目ＩＤ情報５０１と、読取対象となる表記知識情報５０２、認識された最終的な文字列結果情報５０３、各セルバインダの文字切出しグラフを連接することにより作成された文字切出しグラフへのＩＤ情報５０４、この読取項目に含まれるセルバインダ集合の列の先頭セルバインダのＩＤ情報５０５、から構成される。表記知識情報５０２には、読取項目に記入される文字列表記の種類を示し、たとえば本例の場合、住所が記入されるため「Ａｄｄｒｅｓｓ」というデータが格納されている。認識時には本データに従い、文字列表記辞書（図１の１０５）から認識対象の文字列を抽出し、認識処理に利用する。なお、認識対象文字列が自由文章など表記知識を事前に用意できない場合は、表記知識を利用しない旨の「ＮＵＬＬ」が代入されることとする。
【００２２】
セルバインダのデータ構造として、文字列の場合５１０、選択肢の場合５２０の２例を示した。文字列のセルバインダ５１０は図４のセルバインダ４２１に該当し、そのデータ構造は、セルバインダＩＤ情報５１１、セルバインダの認識対象の情報５１２、各セルの文字切出しグラフを連接することにより作成された文字切出しグラフへのＩＤ情報５１６、このセルバインダに含まれるセル集合の列の先頭セルのＩＤ情報５１７、このセルバインダの次に連接するセルバインダのＩＤ情報５１８、から構成される。また選択肢のセルバインダ５２０は図４のセルバインダ４２２に該当し、そのデータ構造は前述の文字列のセルバインダと同様であるが、加えて、選択肢の最大数情報５２３、選択肢の最小数情報５２４、実際に選択された数の情報５２５を有す。
【００２３】
セルのデータ構造として、文字列枠の場合５３０、選択肢枠の場合５４０の２例をしめした。文字列枠のセルバインダ５３０は図４のセル４３１に該当し、そのデータ構造は、セルＩＤ情報５３１、セルの認識対象の情報５３２、セル枠の矩形座標情報５３３、文字切出しグラフへのＩＤ情報５３５、このセルの次に連接するセルのＩＤ情報５３６、から構成される。また選択肢枠のセルバインダ５４０は図４のセルバインダ４３４に該当し、そのデータ構造は前述の文字列枠のセルと同様であるが、加えて、選択肢枠に印刷されている文字列の情報５４５を有す。
【００２４】
以上のデータ構造により、計算機上に枠構造木を構築している。また実際の帳票において、図３の読取項目３１０が複数個存在することがあり、これはさらに複数個の読取項目のデータ構造を用意することにより解決できる。
【００２５】
次に、本実施例における文字認識の処理フローについて説明する（図６）。文字認識処理６００は基本的にボトムアップ処理で、まずステップ６０１で認識対象となる帳票上の全ストロークを入力した後、各セルにおいてセル内のストロークから文字切出しグラフを作成し（ステップ６０５〜６０９）、セルバインダ単位、読取項目単位で連接（各々ステップ６１０、６１１）して最終的に読取項目単位の文字切出しグラフを作成する。その後、その読取項目単位の文字切出しグラフ中に、文字列表記辞書（図１の１０５）内に格納された認識対象文字列が出現するか否かを検出する文字列照合を実行し、文字列認識結果を得て（ステップ６１２）、その結果を出力する（ステップ６１４）といった処理となる。
【００２６】
文字切出しグラフを作成する処理についてより詳細に説明すると、まず各セルにおいて、そのセルの認識対象の情報に応じて、ステップ６０５で文字切出しグラフの作成手順を切替える。文字列枠の場合には、そのセル内に含まれる文字パターンを抽出し（ステップ６０６）、その各文字パターンに対して文字識別処理を実行しその結果から文字切出しグラフを作成する（ステップ６０７）。その例を図７の７０１→７１１、および７０３→７１３に示す。文字切出しグラフの各エッジには、文字パターンとその文字パターンに対する文字識別結果が付加される。文字識別結果として、第１位の文字候補の文字コードのみが付加されていてもよいが、第ｎ位までの文字候補が付加される方が、後で文字列照合する場合に有利となるため、本実施例では各文字パターンに対して複数個の文字識別結果を付加することとする。
【００２７】
一方、選択肢枠の場合は、まず当該の選択肢枠内にマークされているか否かを判定し（ステップ６０８）、その結果マークされていれば、その選択肢に該当する文字列を帳票定義辞書から得て（例：図５の５４４の情報）、その文字列からなる文字切出しグラフを作成する（ステップ６０９）。マークされていない場合には、その選択肢枠についての文字切出しグラフは作成せずに次の記入枠の処理を続ける。その結果、文字切出しグラフが作成されなかった記入枠の前の記入枠とその後の記入枠の文字切出しグラフが連接されることで、例えば本来は選択肢枠で選択されるべき記入事項が前後の記入枠に誤って記入された場合などにも正しく文字認識を行うことが可能となる。その例を図７の７０２→７１２に示す。本来は選択肢の認識結果なので、文字切出しグラフで表現しなくても一意に文字列が同定できるのだが、文字切出しグラフの形式で選択肢枠の認識結果を表現しておくことにより、文字列枠の認識結果と整合性が取れるため容易に連接できるメリットがある。文字切出しグラフの連接は、図７に示したように、単に前の文字切出しグラフの終端ノードと、次の文字切出しグラフの始端ノードをと同一にするのみである。
【００２８】
なお、選択肢の選択数の最大・最小値の検定は、ステップ６１３のリジェクト検定で行われる。
【００２９】
以上が、本発明に係る実施形態の説明である。
【００３０】
【発明の効果】
本発明により、文字列枠と選択肢枠とが混在した帳票の文字認識装置での利用が容易となり、記入者にとって記入しやすい帳票を利用できるため、記入者の負担を軽減される効果がある。
【００３１】
また、記入内容が推測される部分に対し、文字よりも高精度に認識可能な選択肢が利用できるため、記載内容全体の認識率の上昇が期待でき、文字認識装置運用者は文字認識結果の修正作業を軽減できる効果がある。
【００３２】
さらに、文字切出しグラフレベルで各枠の認識結果を統合していくため、より高精度な認識率を期待でき、前記と同様に、文字認識装置運用者は文字認識結果の修正作業を軽減できる効果がある。
【図面の簡単な説明】
【図１】本発明に係る文字認識装置の構成を示す図である。
【図２】本発明に係る実施形態で利用される画像入力部を説明する図である。
【図３】本発明に係る実施形態で利用される帳票の例を示す図である。
【図４】本発明に係る実施形態で利用される帳票における枠構造式と枠構造木を示す図である。
【図５】枠構造木のデータ構造を示す図である。
【図６】本発明に係る文字認識処理のフロー図である。
【図７】文字切出しグラフの連接の過程を示す図である。
【図８】電子ペンによる記入情報のデータ構造を示す図である。
【図９】文字切出しグラフの例を示す図である。[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a character recognition system that reads items such as an address and an amount of money entered on a form or the like.
[0002]
[Prior art]
Handles many forms and texts, such as automatically accepting character strings written on paper to improve work efficiency in the reception of financial institutions and local government offices and the sorting of postal items at post offices. Character recognition technology has been used. In addition, the technique has also been used when reading characters written on a touch panel as input means in a personal digital assistant (PDA).
[0003]
Character recognition mainly includes three steps: (1) character extraction, (2) character identification, and (3) character string collation. First, a pattern that looks like a character is extracted from the input image (character extraction), the character pattern candidate is identified for each character pattern candidate, and the character code is output (character identification). The final recognition result is obtained by interpreting the character string to be read from the set (character string collation).
[0004]
Regarding character extraction, it is difficult to uniquely determine the correct character extraction before character identification and character string matching. Therefore, it is possible to represent multiple hypotheses of possible character extraction in the form of a single network. Cutout graphs are often used to improve reading accuracy. For example, when the character pattern entered in the character string frame 900 of FIG. 9 is cut out, the character pattern 912 of “minute” can be interpreted as two patterns of “eight” 913 and “sword” 914. A graph 910 expresses both of the cut-out hypotheses.
Some of these character recognition systems that read characters have an option recognition function of reading options and check marks, not just reading a character string. For items that can predict the character string to be entered, preprinting the part in advance and letting the writer select it are (1) simpler input, and (2) more accurate choices than characters. This is because there is a merit that it can be recognized. For example, Patent Literature 1 discloses a method for recognizing options.
[0005]
Further, although all predicted character strings cannot be enumerated as options, when the partial character string can be predicted, there is a method of using a reading target item that combines a character string frame and an option frame. As a conventional method for recognizing such combination items, there is a conventional method 1 in which a character string frame and an option frame are simply defined as separate items and individually read. Further, Patent Document 2 discloses a conventional method 2 in which an address is read with high accuracy by narrowing down character string recognition target candidates from the option recognition result.
[0006]
[Patent Document 1] JP-A-6-266884 [Patent Document 2] JP-A-8-243501 [Patent Document 3] International Publication No. 01/71473 [Patent Publication 4] International Publication No. 00/73983 [Problems to be solved by the invention]
However, here, for example, in an address, an address entry consisting of a combination of an option frame that selects only the characters “Miyako”, “Michi”, “Fu”, and “Prefecture” and a character string frame that fills the character string before and after that. Consider the case of reading an item.
[0007]
In this case, in the case of the conventional method 1, for example, when the entrant enters the first half of the character string frame up to the characters “Me”, “Road”, “Fu”, “Prefecture” without noticing the option frame, There was a problem that it could not be read correctly as a whole.
[0008]
Further, in the conventional method 2, since the option recognition result is used for the purpose of narrowing down the character string candidates to be recognized, there is a problem that it cannot be applied to the above case.
[0009]
In addition, since the form layout and the recognition process are closely related in the conventional method, for example, in the case of the address reading, the characters “city”, “ward”, “town”, and “village” are newly added as option frames. When the form layout is changed after addition, there is a problem that the character string notation knowledge to be read and the recognition process itself have to be changed.
[0010]
The present invention has been made in view of the above problems. That is, even when the combination of the character string frame and the option frame is changed, it is possible to respond without changing the reading target character string notation knowledge and the recognition process itself, that is, any character string frame and option frame. It is a first object of the present invention to provide an item reading unit that allows a combination.
[0011]
Furthermore, it is a second object of the present invention to provide a high-precision item reading means corresponding to a variety of entry methods by the writer.
[0012]
[Means for Solving the Problems]
Here, the minimum unit of a character string or option entry frame is referred to as a “cell”. For example, in the example of the application form 300 in FIG. 3, the table 310 includes a character entry frame set 320, a choice frame set 330, and character entry frames 340 and 350, and the character entry frame set 320 is a character entry box. Since the frames 321 to 323 and the option frame set 330 are composed of option frames 331 to 334, the cells in the application form 300 are
・ Text box: 321, 322, 323, 340, 350
・ Option frame: 331, 332, 333, 334
This is a total of 9 frames. When interpreting the text entered and selected in these cells, the recognition result of each cell

The character string entered in Table 310 can be obtained. By the way, the symbol “×” is a symbol representing connection. The expression that expresses the frame structure to be read as in Equation 1 is hereinafter referred to as “frame structure expression”.
[0013]
However, in the options, the maximum / minimum number of selections may be defined as acceptance conditions. For example, in FIG. 3, a choice 330 for a prefecture does not make sense as a place name unless there is at most one mark. Further, since there is a possibility that characters of prefectures are written in the character string frames before and after the option, the minimum number of options is zero. Therefore, it is necessary to specify the maximum number 1 and the minimum number 0.
The frame structure formula is expanded in consideration of such a case. That is, a selection symbol + is newly defined in consideration of selection as well as concatenation. The set of options is enclosed in parentheses (), and the minimum and maximum numbers are entered in brackets [] at the end. That is, the number 1

And expanded. Thereby, it is possible to define a frame structure formula that considers even the number of choices.
[0014]
First, in order to solve the first problem, the present invention provides a form definition dictionary using the frame structure formula that absorbs the difference between a character string frame and an option frame, and a character string by a recognition process along the frame structure formula. It is possible to read items that allow arbitrary combinations of frames and option frames.
[0015]
In addition, in order to solve the second problem, the recognition results of each character string frame and option frame are expressed in the form of a character cut-out graph, and the recognition results of each frame are integrated by combining them, and finally By performing character string matching on the obtained character cutout graph, it is possible to read items with high accuracy.
[0016]
DETAILED DESCRIPTION OF THE INVENTION
A configuration of the character recognition device according to the embodiment of the present invention will be described (FIG. 1). The character recognition device 100 includes an image input unit 101 that electronically inputs written characters and figures, a character recognition unit 102 that reads a designated portion from the input image, and a recognition result output that outputs the recognition result. Part 103. In addition, the form definition dictionary 104 in which information such as the layout of the form to be read and the attributes of each entry frame (what character string is described in the frame, etc.) is described, and the notation knowledge of the character string to be read Is stored and is referred to in character string collation. As the structure of the character string notation dictionary, (1) an enumeration of recognition target character strings, (2) an automaton that accepts the recognition target character strings, and (3) a context free grammar describing the recognition target character strings can be used. .
[0017]
As means for realizing the image input, various means can be used depending on the form of the medium to be entered. For example, when filling in paper, there is a means for capturing a paper image with an optical scanner and a means for placing a tablet under the paper, and when filling in a liquid crystal screen, there is also a means for obtaining handwriting written on a touch panel. . Furthermore, in recent years, there is also a means using an electronic pen that can measure the handwriting when written on a sheet. As an example of the electronic pen, the one disclosed in Patent Document 3 will be briefly described (FIG. 2). Reference numeral 200 denotes an electronic pen, and 201 denotes a camera device that acquires an image of a position detection pattern printed on paper. When the paper 210 is enlarged (211), small dots are scattered inhomogeneously and printed. The dots are printed by shifting a certain distance from the intersection of the virtual grid lines 212 either up, down, left, or right. A plurality of the dots are simultaneously referred to within a range of 6 × 6, for example. The combination of the values of the vertical and horizontal deviations of the dots is a mechanism that gives absolute position information of a vast plane area. That is, by collecting images of a range including the plurality of dot patterns at a constant time interval with the camera 201 provided in the electronic pen, it is possible to specify the position in the space of the combination of the deviations. Details of this principle are clarified in Patent Document 4.
[0018]
The entry information when using electronic means such as an electronic pen, a tablet, and a touch panel for recording a pen locus has a data structure as shown in FIG. The entry information 810 includes the total number of strokes 811 included therein, the number of sample points 812 of each stroke, and a pointer 813 to the sample points. The pointer to the sample point points to one sample 821 of the sample point information 820, and a sample point array from that position to the number specified by the number of sample points is a sample point array. In addition to being able to store dynamic information on the stroke, the electronic pen can store the pen entry information without storing ruled lines, etc., and the pen trajectory without being crushed or crushed like scanning There is an advantage that can be stored. Here, description will be made in the form using the electronic pen.
[0019]
Next, the structure of the form definition dictionary 104 will be described with a specific example. As described above, for example, in the frame structure formula in the frame structure shown in FIG. 3, the cells 321, 322, 323, 331, 332, 333, 334, 340, and 350 are placed as the cells A. ,
A × B × C × (D + E + F + G) [0, 1] × H × I (Equation 3)
You can. If this form is used as a form definition and implemented on a computer, means such as syntax analysis is required when referring to it during processing, which is complicated as an actual problem. Therefore, in this embodiment, the frame structural formula is expressed in the form of a tree structure as shown in FIG. This is a tree structure by dividing each cell into a subset of a character string frame portion and an option portion. Hereinafter, a tree created from the frame structural formula is called a “frame structure tree”, and a subset of cells is called a “cell binder”. For example, in Equation 3 above,

By deforming into the frame structure tree 410, the frame structure tree 410 shown in FIG. 4 can be obtained.
[0020]
Note that the frame configuration tree as shown in FIG. 4 introduces cell binders in both types of subsets of option cells and character string cells, so that the hierarchy of the tree is always a balanced tree with 3 hierarchies. Each cell may be directly connected to the reading item 411 which is the highest node without introducing a cell binder. However, in that case, since all the cells are their children as viewed from the reading item 411 in the top-level clause, branch processing must be performed depending on whether the child is an option cell or a character string cell. Therefore, the introduction of cell binders uniformly to create a three-level balanced tree has the advantage that (1) it is easier to define a data structure representing a tree structure, and (2) the recognition process flow is simpler. For this reason, in this embodiment, a frame structure tree with a cell binder introduced is employed.
[0021]
Furthermore, the data structure of the frame structure tree 410 is shown in FIG. First, the data structure corresponding to the read item 411 is the read item information 500. This data structure is obtained by concatenating read item ID information 501, notation knowledge information 502 to be read, recognized final character string result information 503, and a character cut graph of each cell binder. ID information 504 to the graph, and ID information 505 of the first cell binder in the cell binder set column included in the read item. The notation knowledge information 502 indicates the type of character string notation entered in the reading item. For example, in the case of this example, since an address is entered, data “Address” is stored. At the time of recognition, according to this data, a character string to be recognized is extracted from a character string notation dictionary (105 in FIG. 1) and used for recognition processing. When the recognition target character string cannot prepare notation knowledge such as free text in advance, “NULL” indicating that the notation knowledge is not used is substituted.
[0022]
As the data structure of the cell binder, two examples of 510 for character strings and 520 for options are shown. The cell binder 510 of the character string corresponds to the cell binder 421 of FIG. 4, and its data structure is created by concatenating the cell binder ID information 511, the cell binder recognition target information 512, and the character cutout graph of each cell. ID information 516 to the character cutout graph, ID information 517 of the first cell in the cell set column included in this cell binder, and ID information 518 of the cell binder connected next to this cell binder. The option cell binder 520 corresponds to the cell binder 422 shown in FIG. 4 and has the same data structure as that of the character string cell binder described above. In addition, the option maximum number information 523 and the option minimum number information 524 are used. , The information 525 of the actually selected number is included.
[0023]
As the cell data structure, two examples of a character string frame 530 and an option frame 540 are shown. The cell binder 530 in the character string frame corresponds to the cell 431 in FIG. 4, and the data structure thereof includes cell ID information 531, cell recognition target information 532, cell frame rectangular coordinate information 533, and ID information to the character cutout graph. 535, ID information 536 of the cell connected next to this cell. The cell binder 540 in the option frame corresponds to the cell binder 434 in FIG. 4 and the data structure is the same as the cell in the character string frame described above, but in addition, the character string information 545 printed in the option frame. Have
[0024]
With the above data structure, a frame structure tree is constructed on the computer. Further, there may be a plurality of reading items 310 of FIG. 3 in an actual form, and this can be solved by preparing a data structure of a plurality of reading items.
[0025]
Next, the processing flow of character recognition in the present embodiment will be described (FIG. 6). The character recognition process 600 is basically a bottom-up process. First, all strokes on the form to be recognized are input in step 601, and then a character cutout graph is created from the strokes in the cells in each cell (steps 605 to 609). ), Connected in units of cell binders and units of reading items (steps 610 and 611, respectively), and finally a character extraction graph of units of reading items is created. After that, character string collation is performed to detect whether or not the recognition target character string stored in the character string notation dictionary (105 in FIG. 1) appears in the character cutout graph for each reading item. The recognition result is obtained (step 612), and the result is output (step 614).
[0026]
The process for creating the character cut-out graph will be described in more detail. First, in each cell, the procedure for creating the character cut-out graph is switched in step 605 according to the information to be recognized in the cell. In the case of a character string frame, a character pattern included in the cell is extracted (step 606), character identification processing is executed for each character pattern, and a character cutout graph is created from the result (step 607). . Examples thereof are shown as 701 → 711 and 703 → 713 in FIG. A character pattern and a character identification result for the character pattern are added to each edge of the character cutout graph. As the character identification result, only the character code of the first character candidate may be added, but adding the character candidates up to the nth character is advantageous in the case of character string matching later. In this embodiment, a plurality of character identification results are added to each character pattern.
[0027]
On the other hand, in the case of an option frame, it is first determined whether or not it is marked in the option frame (step 608). If it is marked as a result, a character string corresponding to the option is obtained from the form definition dictionary. (Example: information 544 in FIG. 5), a character cutout graph composed of the character string is created (step 609). If it is not marked, processing of the next entry frame is continued without creating a character cutout graph for the option frame. As a result, the entry frame before the entry frame for which the character cut-out graph was not created and the character cut-out graph of the subsequent entry frame are concatenated. Character recognition can be performed correctly even when the frame is entered incorrectly. An example is shown as 702 → 712 in FIG. Originally, it is a recognition result of a choice, so a character string can be uniquely identified without expressing it in a character cutout graph, but by expressing the recognition result of a choice box in the form of a character cutout graph, There is an advantage that it can be easily connected because it is consistent with the recognition result. As shown in FIG. 7, the character cut graph is simply connected by making the end node of the previous character cut graph and the start node of the next character cut graph the same.
[0028]
It should be noted that the maximum / minimum values of the number of selection options are tested by the reject test in step 613.
[0029]
The above is the description of the embodiment according to the present invention.
[0030]
【The invention's effect】
According to the present invention, it is possible to easily use a form in which a character string frame and an option frame are mixed in a character recognition apparatus, and it is possible to use a form that can be easily filled in for the writer, thereby reducing the burden on the writer.
[0031]
In addition, because options that can be recognized with higher accuracy than characters can be used for the part where the contents are estimated, the recognition rate of the entire description can be expected to increase, and the character recognition device operator can correct the character recognition results. This has the effect of reducing work.
[0032]
Furthermore, since the recognition results of each frame are integrated at the character cut-out graph level, a higher accuracy recognition rate can be expected, and the effect that the character recognition device operator can reduce the work of correcting the character recognition results as described above. There is.
[Brief description of the drawings]
FIG. 1 is a diagram showing a configuration of a character recognition device according to the present invention.
FIG. 2 is a diagram illustrating an image input unit used in an embodiment according to the present invention.
FIG. 3 is a diagram showing an example of a form used in an embodiment according to the present invention.
FIG. 4 is a diagram showing a frame structure formula and a frame structure tree in a form used in the embodiment according to the present invention.
FIG. 5 is a diagram illustrating a data structure of a frame structure tree.
FIG. 6 is a flowchart of character recognition processing according to the present invention.
FIG. 7 is a diagram illustrating a process of connecting character cutout graphs.
FIG. 8 is a diagram illustrating a data structure of entry information by an electronic pen.
FIG. 9 is a diagram illustrating an example of a character cut-out graph.

Claims (4)

Creation of a character cutout graph indicating the possibility of a character cutout pattern for each entry frame using a pre-registered form definition for a predetermined portion of the image input form, and the character cutout based on predetermined frame structure information A form definition used in a character recognition device that performs character recognition by performing character string matching on a connected graph and a connected character cut-out graph,
Including a box for recognizing a character string and a plurality of boxes including a box for recognizing an option;
The entry frame information includes the recognition object of the entry frame and the frame structure information indicating the order of connection in the form,
When the recognition target of the entry box is a character string, a character identification process is performed on the character pattern included in the entry box to create a character cut-out graph,
A form definition characterized in that when a recognition target of the entry frame is an option, a character cutout graph including a character string corresponding to the option marked in the entry frame is created. A character recognition method for electronically taking in characters and option marks entered in a form having a character string entry box and an option entry frame in accordance with a form definition in which information of the read item is described, and reading the item on the form In
The form definition includes entry frame information including a recognition target of the entry frame for a plurality of entry frames including an entry frame that recognizes a character string and an entry frame that recognizes an option,
The entry frame information includes frame structure information indicating the recognition target of the entry frame and the order of connection in the form,
Create a character cutout graph corresponding to the character string or choice of each box according to the box information described in the form definition above,
Concatenating or selecting the character string cut-out graph according to the frame structure information described in the form definition,
A character recognition method comprising interpreting a character string of the concatenated or selected character string cutout graph. The character recognition method according to claim 2,
When the target of recognition of the entry frame is an option, it is determined whether or not there is a mark in the entry frame. If there is a mark, a character cutout graph corresponding to the marked option is created and there is no mark. In some cases, the character recognition method does not create a character cutout graph corresponding to the entry frame. A character recognition device that electronically captures characters entered in a form and marks of choices to perform character recognition,
A memory in which a form definition in which information of a reading item is written is registered in advance;
A character recognition unit that reads items on the form in accordance with the form definition,
The form definition includes entry frame information including a recognition target of the entry frame for a plurality of entry frames including an entry frame that recognizes a character string and an entry frame that recognizes an option,
The entry frame information includes the recognition object of the entry frame and the frame structure information indicating the order of connection in the form,
The character recognition part
When the recognition target of the entry frame is a character string, a character identification process is performed on the character pattern included in the entry frame to create a character cut-out graph,
If the recognition target of the entry box is an option, create a character cutout graph that includes the character string corresponding to the option marked in the entry box,
Concatenating the character cut-out graph based on the frame structure information,
A character recognition apparatus, wherein character recognition is performed by performing character string matching on the connected character cutout graphs.

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