JPH1027216A - Character pattern recognition processing method and apparatus - Google Patents

Abstract

(57) [Summary] [PROBLEMS] To make it difficult to be affected by the inclination of a character line or a change in a connection relationship, and to be able to recognize a character object with many handwritten deformations. A binarized character pattern is divided into a plurality of coarse mesh areas (301). With respect to the black pixels of the character portion and the white pixels of the background portion forming the contour in each mesh area, the tentacles are extended in a plurality of predetermined directions to determine the connection length of the white pixels for each direction (302). From the white pixel connection length, the value of the directional contribution representing the distribution state of each directional component of the background portion of the white pixel is counted for each mesh area, and is set as a character pattern feature (303). This feature represents the two-dimensional structure of the character, is less susceptible to changes in the inclination of the character line and changes in the connection relationship, and makes it possible to recognize character objects that are frequently deformed by handwriting.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and an apparatus for recognizing a character pattern, and more particularly to a character pattern obtained by binarizing a character pattern obtained by photoelectric conversion. The present invention relates to a character pattern recognition method and apparatus suitable for recognizing a character object having a handwritten deformation.

[0002]

2. Description of the Related Art Various methods have been conventionally proposed for character pattern recognition processing, one of which is a mesh division method. Conventionally, the following method is known as a typical method.

A first method is to divide a character pattern, which has been binarized and normalized in position and size, into a plurality of coarse mesh areas and to apply a character pattern existing in each mesh area in a plurality of directions. Observed from the coordinate axis, the number of character lines crossing the character portion in the direction orthogonal to the coordinate axis at each position on the coordinate axis is counted, a feature vector pattern is created from this information, and the characteristics of each character already stored This is a method of matching a dictionary table and recognizing a character pattern.

In a second method, a character pattern which has been binarized and the position and size of which have been normalized is divided into a plurality of coarse mesh areas, and a character portion existing in each mesh area is divided. This is a method of recognizing a character by observing from a plurality of coordinate axes and obtaining the directional contribution of a character line for a black pixel of a character portion crossed when scanning from the coordinate axis.

[0005]

In the above-mentioned prior art, in the first method, although the difference in the general complexity of the character line structure due to the difference in the character type can be distinguished by the number of character lines crossing the character portion, the method is more detailed. Since there is no information indicating a difference in a character line structure, there is a problem that a character object having many similar characters and many handwriting deformations cannot be recognized well. In addition, the second method has a problem in that a character object having many handwritten deformations such as a tilt of a character line and a change in a connection relationship cannot be recognized well.

An object of the present invention is to obtain information on the two-dimensional structure of a character from a binarized character pattern whose position and size have been normalized, and to obtain a change in the inclination of a character line and a connection relationship. It is an object of the present invention to provide a character pattern recognition method and apparatus that can recognize a character object having a large amount of handwritten deformation such as a tilt of a character line or a change in a connection relationship by using a feature that is not easily affected.

[0007]

In order to achieve the above-mentioned object, the present invention provides a method for producing a character pattern comprising: a directional contribution of a black point of a character portion and a white point of a background portion forming an outline; It is characterized in that information on mutual arrangement of character lines is obtained by calculating for each area. As a result, a two-dimensional structure of the character can be obtained, and the character is hardly affected by the inclination of the character line or the change in the connection relationship. Therefore, it is possible to recognize a character object that is frequently deformed by handwriting.

[0008]

An embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is an overall configuration diagram for explaining an embodiment of the present invention. In the figure, 10 is a binarized input character pattern, 20 is a preprocessing unit, 30 is a feature extraction unit, 40 is an identification processing unit, 50 is a feature dictionary table, 60
Is the identification result.

The preprocessing unit 20 uses, for example, a conventionally known position normalization processing method to input the input character pattern 1.
The average value of each of the x and y coordinate values of all the black points constituting 0 is calculated and defined as the center of gravity of the character. Then, the parallel movement processing of the entire input character pattern is performed so that the center of gravity is located at the center position of the character frame. Do. Similarly, using a conventionally known size normalization processing method, one point around the center of gravity is set so that the average value of the distance from the center of gravity to each writing point is equal to a predetermined normalized radius. The character pattern is enlarged / reduced as described above. Further, when the character pattern after the position and size normalization processing is out of the character frame, the pre-processing unit 20 performs a process of removing the character part which is out of the character frame (frame processing). In addition, a process (smoothing process) of filling or removing the unevenness of one mesh of the black point of the character line contour portion of the character pattern after the position and size normalization process is performed.

FIG. 2 shows the pre-processing unit 2 for the character "tree".
An example in which the input character pattern 10 is normalized by the normalization process of 0 is shown. FIG. 2A is an example of the input character pattern 10. FIG. 2B shows a character pattern after the preprocessing unit 20 has performed a position and size normalization process on the input character pattern 10. FIG. 2 (c)
FIG. 2D shows an example in which the character pattern after the position and size normalization processing has run out of the character frame, FIG. 2D shows an example in which the frame processing is performed on the character pattern, and FIG. Each example is shown.

The feature extracting section 30 is a main part of the present invention. The character extracting section 30 inputs the character pattern subjected to the normalization processing in the preprocessing section 20, and divides the character pattern into a plurality of coarse mesh areas. Then, the tentacles are extended in a plurality of predetermined directions with respect to the black point of the character portion and the white point of the background portion forming the contour in each mesh area, the number of white points connected in each direction is counted, and the direction of the white pixel is counted. Perform the process of calculating the contribution.

FIG. 3 shows a processing flowchart of the feature extracting unit 30. Here, reference numeral 301 denotes a process of dividing a character pattern into a plurality of coarse mesh regions, and reference numeral 302 denotes a plurality of black pixels (black points) of a character portion and a plurality of white pixels (white points) of a background portion forming an outline in each mesh region. Direction (for example, 0 °, 45 °, 90 °, 135 °, 1
(8 directions of 80 °, 225 °, 270 °, and 315 °) to extend the tentacles to determine the connection length of the white pixel for each direction. 303 is a component of each direction of the background portion of the white pixel from the white pixel connection length. This is a process in which the value of the directional contribution degree representing another distribution state is counted for each mesh region, and is set as a feature of the character pattern. The specific processing algorithm of the feature extraction unit 30 will be described later.

The identification unit 40 creates a feature table for identifying a character pattern based on the value of the directional contribution obtained by the feature extraction unit 30, and stores the feature table based on the feature table. Character dictionary table 50 for each character
Then, matching is performed by a conventionally known matching method to identify a character pattern.

Next, as a processing algorithm of the feature extraction unit 30, the character pattern is divided into K coarse mesh areas, and black points of the character parts in each mesh area and white points of the background part which forms the outline are divided into eight. Direction (0 °, 45 °, 90
°, 135 °, 180 °, 225 °, 270 °, 315
The case where the tentacles are extended to (°) to obtain the directional contribution of the background portion and the character pattern is identified will be described.

FIG. 4 shows N obtained by the preprocessing unit 20.
This figure shows how a character pattern of × N mesh is equally divided into K coarse mesh areas, for example, square mesh areas. In FIG. 4, 1, 2,..., K,. FIG. 5 shows eight directions (0 °, 45 °, 90 °) of extending the tentacle to obtain the white point connection length.
°, 135 °, 180 °, 225 °, 270 °, 315
FIG. 6 is a diagram showing the manner in which the black point of the character portion and the white point connection length of the background portion forming the contour are obtained.
In the following, 0 °, 45 °, 90 °, 135 °, 180
225 °, 270 °, and 315 ° are numbered 1, 2, 3, 4, 5, 6, 7, and 8, respectively.

The k-th (1, 2,..., K,.
K), the directional contribution f of the black point of the character portion and each white point of the background portion forming the contour in the mesh area is f = (α1, α2, α3, α4, α5, α6, α7, α)
8) It is represented by the following eight-dimensional vector. Where α1, α2,
..., α8 are directional contribution components in eight directions, respectively.
The tentacles are extended in eight directions from the corresponding white point, and the white point connection length li (i = 1, 2,..., 8) obtained for each direction is used as an example.

[0017]

(Equation 1)

## EQU1 ## It is also possible to apply a distance other than the Euclidean distance shown here to αi.

The f obtained in this manner is obtained for the black point of the character portion in each mesh area and all the white points of the background portion forming the contour, and is accumulated. By averaging by the number, the characteristic pattern f obtained in the k-th mesh area
k is represented by fk = (αk1, αk2,..., αk8). Here, αk1, αk2,..., Αk8
Each element of the directional contribution vector obtained by accumulating the directional contribution vectors at the black points of the character part existing in the k-th mesh area and all the white points of the background part forming the contour for each directional component, or Each element of the direction contribution vector accumulated for each direction component is averaged by the number of white points. Therefore, the feature vector F of the character pattern is represented by F = (f1, f2,..., Fk,..., FK).

A feature table is created using the values of the respective elements of the feature vector F of the character pattern represented in this way as features of the character pattern, and the identification unit 40 uses, for example, the Euclidean distance as a conventionally known identification function. And the like, and a character pattern is identified.

Here, the discrimination function calculates a distance value between the feature vector of the input character pattern and the feature vector for each character type stored in the feature dictionary table 50 in advance, and determines the smallest distance value (function The character with the largest value is output as a candidate character.

The feature vector of the input character pattern obtained by the feature extracting unit 30 is represented by F = (f1, f2,..., Fk,
.., FK), each character i (1) in the feature dictionary table 50
≦ i ≦ M) is defined as Si = (si1, si2,
.., SiK), for example, in the case of the Euclidean distance, in the identification unit 40, between the character types from i = 1 to M,

[0023]

(Equation 2)

Is calculated, and the character type of i having the smallest value is output as a correct character pattern.

Next, an application example of another algorithm in the feature extraction unit 30 will be described. The N × N mesh character pattern obtained by the preprocessing unit 20 is equally divided into K coarse mesh areas, for example, square mesh areas.
In this algorithm, the k-th (1, 2,..., K,
.., K), the directional contribution g of the black point of the character portion and each white point of the background portion forming the contour in the mesh area is represented by a four-dimensional vector g = (β1, β2, β3, β4) . Where β1, β2, ...
Β4 is a directional contribution component in each of the four directions, extending the tentacle in eight directions from the corresponding white point, and using the white point connection length li (i = 1, 2,..., 8) obtained for each direction. ,As an example

[0026]

(Equation 3)

## EQU2 ## Note that a distance other than the Euclidean distance shown here can be applied to βi.

The g thus obtained is obtained for the black point of the character portion in each mesh region and all the white points of the background portion forming the contour, and is accumulated. The characteristic pattern gk obtained in the k-th mesh area by averaging with the number of white points in is represented by gk = (βk1, βk2, βk3, βk4). Here, βk1, βk2, βk3, βk4
Each element of the directional contribution vector obtained by accumulating the directional contribution vectors at the black points of the character part existing in the k-th mesh area and all the white points of the background part forming the contour for each directional component, or Each element of the direction contribution vector accumulated for each direction component is averaged by the number of white points. Therefore, the feature vector G of the character pattern is represented by G = (g1, g2,..., Gk,... GK).

A characteristic table is created using the values of the respective elements of the characteristic vector G of the character pattern represented as described above as the characteristics of the character pattern, and the identification unit 40 uses, for example, a Euclidean distance as a conventionally known identification function. And the like, and a character pattern is identified.

[0030]

As described above, according to the present invention, the relative position of the halftone character and the shape in the vicinity of the outline can be extracted by obtaining the directional contribution of the background portion. Is less likely to be affected by the character, it becomes possible to recognize a character object with a large amount of handwritten deformation.

[Brief description of the drawings]

FIG. 1 is an overall configuration diagram showing an embodiment of the present invention.

FIG. 2 is a diagram illustrating a state of preprocessing in a preprocessing unit.

FIG. 3 is a processing flowchart in a feature extraction unit.

FIG. 4 is a diagram illustrating a manner in which a character pattern is divided into coarse mesh regions in a feature extraction unit.

FIG. 5 is a diagram showing a case where eight directions are set as directions in which a tentacle is extended in order to obtain a white point connection length in a feature extraction unit.

FIG. 6 is a diagram illustrating a manner in which a feature extraction unit obtains a white point connection length of a background part forming a black point and an outline of a character part.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 10 Input character pattern 20 Preprocessing part 30 Feature extraction part 40 Identification part 50 Feature dictionary table 60 Identification result

Claims (2)

[Claims] 1. A binarized character pattern is divided into a plurality of mesh areas. In each mesh area, black pixels of a character portion and white pixels of a background portion forming an outline are defined in a plurality of predetermined directions. Extend the tentacles to determine the connection length of white pixels in each direction, and count the value of the direction contribution representing the distribution of each direction component of the background of the white pixels obtained from the white pixel connection length for each mesh area A character pattern recognition processing method characterized by recognizing a character pattern by using the value of the direction contribution. 2. A pre-processing unit for performing a normalization process on a position and a size of a character in a binarized character pattern, and dividing the normalized character pattern into a plurality of mesh regions. In the black portion of the character portion and the white portion of the background portion forming the contour, the tentacles are extended in a plurality of predetermined directions to determine the connection length of the white pixel in each direction, and the connection length of the white pixel is calculated from the white pixel connection length. A feature extraction unit that counts the value of the directional contribution degree representing the distribution state of each directional component of the background part for each mesh region and makes the feature of the character pattern a feature;
A character pattern recognition device, comprising: an identification unit that performs an identification process using the feature.