WO2008020557A1 - Procédé de reconnaissance d'écriture manuscrite, système de reconnaissance d'écriture manuscrite, programme de reconnaissance d'écriture manuscrite, et support de données - Google Patents

Procédé de reconnaissance d'écriture manuscrite, système de reconnaissance d'écriture manuscrite, programme de reconnaissance d'écriture manuscrite, et support de données Download PDF

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Publication number
WO2008020557A1
WO2008020557A1 PCT/JP2007/065458 JP2007065458W WO2008020557A1 WO 2008020557 A1 WO2008020557 A1 WO 2008020557A1 JP 2007065458 W JP2007065458 W JP 2007065458W WO 2008020557 A1 WO2008020557 A1 WO 2008020557A1
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WO
WIPO (PCT)
Prior art keywords
point
angle
node
character
series
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Application number
PCT/JP2007/065458
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English (en)
Japanese (ja)
Inventor
Shunji Mori
Tomohisa Matsushita
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Kite Image Technologies Inc.
Priority date (The priority date 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 date listed.)
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Publication date
Application filed by Kite Image Technologies Inc. filed Critical Kite Image Technologies Inc.
Publication of WO2008020557A1 publication Critical patent/WO2008020557A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/32—Digital ink
    • G06V30/333—Preprocessing; Feature extraction
    • G06V30/347—Sampling; Contour coding; Stroke extraction

Definitions

  • Handwriting character recognition method handwriting character recognition system, handwriting character recognition program and storage medium
  • the present invention relates to a handwritten character recognition method and handwritten character recognition system for performing online handwritten character recognition, a handwritten character recognition program for realizing the recognition method, and a storage medium storing the program.
  • the pattern matching method is roughly divided into two types. As described in the journal of the Institute of Electronics, Information and Communication Engineers, J63-D, 2, pp. 153-160, "On-line recognition of handwritten characters by point approximation of strokes", approximating strokes with a few points
  • the motion direction of the brush at the end point is estimated with the feature point as the feature point, and they are also made into special focus to construct a feature vector.
  • the dictionary is decomposed into strokes, and they have feature vectors as well, and the input vector is correlated with the feature vector prepared for each category, the distance is calculated for the corresponding dictionary, and the minimum distance is calculated.
  • the given dictionary name is the recognized character name, and it is basically free for stroke order and stroke count.
  • an image seen by a human being is two-dimensional force, which is strictly on the time axis, and is one-dimensional. That is, it can be expressed as a simple one-dimensional linear graph. This point of view dramatically simplifies the problem. Moreover, due to the winding angle, natural clipping candidate points are arranged in a negative manner on the time axis.
  • the winding angle and the linear (one-dimensional) graph are the core of the present invention.
  • the present invention was made in view of force, and basically belongs to the structural analysis method described above, but overcomes the problems so far and is flexible structural analysis It provides the basis for the method, and therefore aims to avoid symbolization problems, represent structures analogically, and perform flexible and simple matching with standards.
  • input handwritten character strings are captured for each stroke by parameter expression, and each line is subjected to polygonal line approximation, and each polygonal line is approximated by polygonal line.
  • the angle between the reference axis and each polygonal line is determined as a polygonal line series as the vector extending from the start point to the end point, and the external angle series of each vertex of the obtained polygonal line is determined.
  • the sum of the external angles of the same code where the codes are continuous is taken as a winding angle series, and based on the feature extraction by each series obtained, the point which is a reference point is no
  • the graph representation is given by the attribute as the point of the node and the attribute as the edge between the nodes, and the start point and the end point are not particularly defined! /
  • the present invention when recognizing on-line handwritten characters, it is flexible and noise at the end, even for characters that maintain normal upper / lower and left / right relationships, and also when rotation invariance is required. In the same way as the recognition method of the character written in isolation, it makes strong recognition in the continuous character string and makes strong recognition to the character and transformation. it can.
  • FIG. 1 is a block diagram showing an example of a system according to an embodiment of the present invention.
  • FIG. 2 is a flow chart showing an example of processing of the entire character recognition according to an embodiment of the present invention.
  • FIG. 3 is a flowchart showing a detailed example of character recognition processing according to the embodiment of the present invention.
  • FIG. 4 is an explanatory drawing showing an example of polygonal line approximation according to an embodiment of the present invention.
  • FIG. 5 is an explanatory diagram showing an example of a node according to an embodiment of the present invention.
  • FIG. 6 is an explanatory drawing showing an example of polygonal line approximation according to an embodiment of the present invention.
  • FIG. 7 is an explanatory drawing showing an example of polygonal line approximation according to an embodiment of the present invention.
  • FIG. 8 is an explanatory drawing showing an example of polygonal line approximation according to an embodiment of the present invention.
  • FIG. 9 is an explanatory drawing showing an example of the relationship between nodes and sides according to an embodiment of the present invention.
  • FIG. 10 is an explanatory view showing a determination example according to an embodiment of the present invention.
  • FIG. 1 shows an example of a configuration in which each processing unit has a hardware configuration.
  • the handwritten character recognition of this example is programmed in a general-purpose arithmetic processing unit such as a personal computer device or a general-purpose arithmetic processing unit in which each processing unit is executed by a common arithmetic processing unit.
  • a general-purpose arithmetic processing unit such as a personal computer device or a general-purpose arithmetic processing unit in which each processing unit is executed by a common arithmetic processing unit.
  • writing on the paper 1 with the pen 2 detects the pen stroke (la) on the paper 1 on the pen 2 side.
  • the pen is detected by, for example, a pen 4/1
  • the camera built in 2 It does with the camera built in 2.
  • the movement of the force pen 2 itself such as an acceleration sensor may be detected.
  • the side of the paper 1 that is not detected by the pen is configured with any force panel, it is possible to detect the handwriting electrically.
  • it since it is online handwriting character recognition, it is configured to be able to judge the deterioration of the handwriting over time.
  • the handwriting data detected by these processes are sent to the input processing unit 3 to output character information : a second input is performed.
  • the input data is sent to the polygonal line approximation unit 4, the eyebrow extraction unit 5, the identification unit 6, and the identification result output unit 7, and corresponding processing is performed in each of the processing units.
  • the identification result output unit 7 performs output processing such as display of the identified characters and 'output of the character code of the identified I. Marking or printing of the identification character may be performed based on the identified character code.
  • the flow chart of FIG. 2 shows an example of the entire processing of the character recognition of this embodiment.
  • the character / graphic pattern input from the input processing unit 3 is subjected to polygonal line approximation by the polygonal line approximation unit 4 (step S12). From this approximation, the input pattern is expressed as a vector having the length, the direction angle, and the difference in the direction angle of the adjacent line as an element when viewing each line as a vector (step S13). Also, from the vector expression of the difference of the direction angle, the sum of the difference of the same sign is obtained, and as one element including the code, the vector expression named as the winding angle is obtained here.
  • step S14 features are extracted according to the situation from the polygonal line approximation representation in the feature extraction unit 5 (step S14), and a one-dimensional linear graph representation based on the extraction results of the features is given (step S15).
  • Character recognition is performed by matching the expression with a template based on an open mask configuration that does not particularly define the start point and the end point (step S16), and the character recognition result is output (step S17).
  • step S 16 it is checked that the arrangement of each node shape of the input graph expression matches (step S21). Then, the matching of the attribute as the node point is checked (step S22), and then the matching of the attribute of the edge between the nodes is checked (step S23). Finally, the matching of other attributes such as the presence or absence of the intersection point and the distance relationship between nodes is checked (step S24) and identified. If all the results of these checks match, the recognition result is OK (step S25), and if there is not even one match, it is excluded (step S26).
  • the recognition method in the present embodiment basically belongs to the structural analysis method described above, but it overcomes the problems so far and provides a basis for a flexible structural analysis method. It is a thing. Therefore, it avoids the problem of symbolization, expresses the structure in an analog manner, and performs flexible and simple matching with the standard. In addition, structural analysis is performed, so that the subject can inevitably be described properly, and the correspondence between cause and effect is clear from the human vision. Therefore, it is possible to evaluate the shape of objects such as letters, set the correct rejection range, and provide a recognition system with more human-like ability.
  • this method is very convenient for efficient character recognition in continuous character strings (eg cursive letters etc.).
  • the winding angle makes it possible to Focusing on the fact that the extraction candidate points are positively aligned in the negative, the input character string is one-dimensionally linear using the attributes of the candidate points (nodes) and the edge between the nodes (edges)
  • a graph representation is given, segmentation is performed simultaneously with recognition ("segmentation recognition"), and segmentation can be performed in the same way as the recognition method for isolated characters written before preprocessing. is there.
  • the image in Fig. 4 is a force written from the left.
  • the basic representation of these is the length of each polygonal line to be connected, the polygonal line angle, the outer corner composed of the adjacent polygonal line corners, and the same sign.
  • the length series, the angle series, the outer angle series, and the winding angle series that have the winding angle as an element, which is the sum of the outside angles of the issue.
  • a start point an end point, a point giving a winding angle of an integral multiple of 90 degrees ( ⁇ n ⁇ 90 degrees long point), a point where the sign of the winding angle changes is a “node”, and that point is used as a node attribute.
  • [s] and [e] are start and end symbols, respectively.
  • [-] is the first winding angle-90 degree point in the outer angle ( ⁇ ) series. Since the winding angle is discrete linear interpolation, it can generally be obtained in analog form as an ⁇ degree long point. 1 ⁇ [-] "one" is a left angle mark Express the issue.
  • Condition 2 -200 ⁇ (*-) ⁇ 100 & 200 ⁇ (* +) ⁇ 360 (Note: “ ⁇ (*-)” is the first "" winding angle, “ ⁇ (* +)” is the succeeding "+” winding angle)
  • Condition 3 -100 mm ⁇ (* _) K -20 & 10 ⁇ (* +) 100
  • condition 4 the values of the winding angle, at both ends of the boundary line segment.
  • condition 4 the values of the winding angle, at both ends of the boundary line segment.
  • intersection point information is used as an important edge property. That is Condition 4 and Condition 8.
  • me (* + l +) to (* + l ⁇ ) require that an intersection be present between the boundary lines at the next winding angle change point.
  • Cross the intersection with Cross nxm expressing.
  • the n and m are the numbers of intersecting polygonal lines, and ne (*-) to (* +) indicate that the intersecting polygonal lines coincide with the winding angle boundary line segments.
  • the intersection is a rotation invariant feature.
  • the mask mentioned above does not include the characteristic of full distance or length. Therefore, regardless of the length of the connection, the shape was fitted everywhere, and the cut-out recognition was made. However, on the other hand, for example, as in the case of Fig. 7, the information of two "8" sizes is lost, and there is a doubt that the difference in size can not be understood as seen by humans. However, this can actually be easily determined.
  • This input representation has position coordinates of each node. Therefore, for example, in the case of "8", the node returned 180 degrees from the "*" node which is the mask key, and in FIG. 7, this is almost an ⁇ s> node. Find the Euclidean distance between the positions of
  • This mask is made as follows.
  • Condition 12 (* + 3. * 4); 0 ⁇ 0 ⁇ Length 0.35 & 0 ⁇
  • Sign " ⁇ ” condition 1 & condition 2 & condition 3 & condition 4 & condition 5 & condition 6 & condition Condition 7 & Condition 8 & Condition 9 & Condition 10 & Condition 11 & Condition 12
  • This mask is sufficiently configured to be resistant to noise! /, And both ends are open //.
  • each length is normalized relative to the length as described above, for example, with the distance between the 180 ° long point and the 360 ° long point, this can be an arbitrary number of concatenated character sequences. Is also applicable.
  • the upper and lower irregularities are indicated by u and ⁇ , and the irregularities viewed from the left and right are indicated by ⁇ and c.
  • the upper and lower asperities and the left and right asperities simultaneously exist.
  • n + co where n and one overlap is indicated.
  • the attributes of the nodes are as described above. As the attributes of the force side, the average direction angle of the polygonal line group between the nodes is added. This is a very important feature in concavo-convex node expression. Others There is a directional variance of the polyline group indicating the degree of bending. Next important is the distance ratio between nodes. For example, referring to Figure 9, the start point must be somewhat above the end point. These can be quite large when calculated mechanically, but in practice they can not be good numbers if only the point is kept down. Here, for example, the start point ⁇ s> node Y axis value is subtracted from the Y axis value of node 5>, and the value obtained by dividing by the ratio of the length of the whole character is obtained.
  • Condition 23 ((* + 1, * + 2) Cross Y value-(*) Y value) / Height 0 ⁇ 20 ⁇ ⁇ 0 ⁇ 70 ⁇ The intersection is located approximately at the center in height.
  • Letter '8' condition 1 & condition 2 & condition 3 & condition 4 & condition 5 & condition 6 & condition 7 & condition 8 & condition 9 & condition 10 & condition 11 & condition 12 & condition 13 & condition 14 & condition 14 & condition 15 & Condition 16 & Condition 1 7 & Condition 18 & Condition 19 & Condition 20 & Condition 21 & Condition 22 & Condition 23
  • the inter-node distance ratio is actually much less than the number produced in combination. That is because it is restricted within silence by the restrictions of wrap angle, outside angle, and line angle. Also, the length restriction in the upper mask only limits the length of the polyline, which goes from left to right. In fact, because of this, very, strong noise on the edge, it becomes a mask!
  • the cut-out recognition method described above necessarily has the possibility to give multiple answers.
  • FIG. Figure 10 A specific example of this is shown in FIG. Figure 10 is written with the intention of “8”. However, “6” is hidden in this figure. If you change the word, the character form "6" is "cut out and recognized” from the original figure. Therefore, this is an inevitable result.
  • the handwritten character recognition of the present invention is substantially the same handwritten character recognition that is not limited to the processing configuration shown in FIG. To perform recognition processing with various device and system configurations. It is possible.
  • the handwritten character recognition of the present invention is converted into a program (software) to make a general-purpose personal computer.
  • the handwritten character recognition program can be stored in various storage media and distributed.
  • the character recognition may be performed on the off-line characters by the force S for the on-line characters, appropriate thinning, or contour tracing.
  • handwritten character recognition of the present invention can be basically applied to characters and symbols of any language.

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Character Discrimination (AREA)

Abstract

Le problème de la symbolisation est évité, la structure est exprimée d'une manière analogue, et la correspondance avec une référence peut être effectuée de manière flexible et simple. Chaque trait d'une chaîne de caractères manuscrits est compris dans une expression de paramètres. Chacun des traits est approximé en une ligne brisée. La ligne brisée dessinée par l'approximation de la ligne brisée est traitée en tant que vecteur à partir d'un point de départ jusqu'à un point d'arrivée. Les angles entre un axe de référence et les lignes brisées sont déterminés comme une séquence d'angles de lignes brisées. La séquence d'angles extérieurs des sommets des lignes brisées obtenues est déterminée. La somme des angles extérieurs des mêmes signes + ou - continus de la séquence d'angles extérieurs est définie en tant que séquence d'angles d'enroulement. En se basant sur l'extraction des caractéristiques par les séquences déterminées, les nœuds à utiliser comme points de référence sont déterminés. On obtient une expression graphique à partir des attributs des positions des nœuds et des attributs des arêtes entre les nœuds. La correspondance avec un modèle ayant une structure de masque ouvert dans laquelle le point de départ et le point d'arrivée ne sont pas particulièrement définis, est effectuée de telle sorte que la reconnaissance de caractères soit flexible et robuste vis-à-vis du bruit aux extrémités et de la déformation même si le caractère est de ceux qui possèdent des relations haut-bas et gauche-droite normales et même si l'invariance par rotation est nécessaire.
PCT/JP2007/065458 2006-08-14 2007-08-07 Procédé de reconnaissance d'écriture manuscrite, système de reconnaissance d'écriture manuscrite, programme de reconnaissance d'écriture manuscrite, et support de données WO2008020557A1 (fr)

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JP2006-221253 2006-08-14
JP2006221253A JP5352757B2 (ja) 2006-08-14 2006-08-14 手書き文字認識方法、手書き文字認識システム、手書き文字認識プログラム及び記憶媒体

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107067046A (zh) * 2016-11-29 2017-08-18 南京工程学院 基于混合特征提取的手写数字识别方法
CN113392772A (zh) * 2021-06-17 2021-09-14 南开大学 一种面向文字识别的文字图像收缩变形增强方法

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS6395591A (ja) * 1986-10-13 1988-04-26 Wacom Co Ltd 手書き文字認識方法
JPS642187A (en) * 1987-06-24 1989-01-06 Nec Corp On line successive character recognition device
JPH06274698A (ja) * 1993-03-18 1994-09-30 Pfu Ltd オンライン手書き文字認識方式
JPH08101889A (ja) * 1994-09-30 1996-04-16 Sharp Corp オンライン手書き文字認識方法
JPH1049631A (ja) * 1996-05-22 1998-02-20 Seiko Epson Corp オンライン手書き文字認識方法および装置
WO2006088222A1 (fr) * 2005-02-15 2006-08-24 Kite Image Technologies Inc. Procédé de reconnaissance de caractères manuscrits, système de reconnaissance de caractères manuscrits, programme de reconnaissance de caractères manuscrits et supports d’enregistrement

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS6395591A (ja) * 1986-10-13 1988-04-26 Wacom Co Ltd 手書き文字認識方法
JPS642187A (en) * 1987-06-24 1989-01-06 Nec Corp On line successive character recognition device
JPH06274698A (ja) * 1993-03-18 1994-09-30 Pfu Ltd オンライン手書き文字認識方式
JPH08101889A (ja) * 1994-09-30 1996-04-16 Sharp Corp オンライン手書き文字認識方法
JPH1049631A (ja) * 1996-05-22 1998-02-20 Seiko Epson Corp オンライン手書き文字認識方法および装置
WO2006088222A1 (fr) * 2005-02-15 2006-08-24 Kite Image Technologies Inc. Procédé de reconnaissance de caractères manuscrits, système de reconnaissance de caractères manuscrits, programme de reconnaissance de caractères manuscrits et supports d’enregistrement

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107067046A (zh) * 2016-11-29 2017-08-18 南京工程学院 基于混合特征提取的手写数字识别方法
CN113392772A (zh) * 2021-06-17 2021-09-14 南开大学 一种面向文字识别的文字图像收缩变形增强方法
CN113392772B (zh) * 2021-06-17 2022-04-19 南开大学 一种面向文字识别的文字图像收缩变形增强方法

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