Method for character recognition based on gabor filters
First Claim
1. A method for character recognition based on a Gabor filter group, said method comprising:
- (a) pre-processing a character image, said pre-processing including receiving, pre-processing the character image of a character to be recognized, and obtaining a binary or gray image with N×
N pixels for each character to be recognized, wherein said binary or gray image for each character to be recognized is represented by a matrix [A(i, j)]N×
N;
(b) processing the matrix [A(i, j)]N×
N for the character image of each character to be recognized obtained in the step (a), said processing including;
extracting stroke direction information of the character to be recognized, said extracting including employing the Gabor filter group which is composed of K two-dimension Gabor filters to extract stroke information in K different directions from the matrix [A(i, j)]N×
N of the character image of the character to be recognized, and obtaining K matrixes [Gm(i, j)]M×
M, m=1 . . . K, of the character image of the character to be recognized, wherein each of said matrixes [Gm(i, j)]M×
M, m=1 . . . K, possesses M×
M pixels and represents the stroke information of one of the K directions;
extracting features from blocks, including the steps of;
(1) evenly dividing each of said K matrixes [Gm(i, j)]M×
M into P×
P rectangular areas which are overlapped with each other and have a length L for each side of each rectangular are;
(2) respectively calculating a first weighted sum of positive values and a second weighted sum of negative values of all pixels within the area, at the center of each rectangular area;
(3) forming a first feature vector Sm+ of positive values and a second feature vector Sm−
of the negative values according to the first weighted sum and the second weighted sum of each rectangular area of each matrix, wherein the dimensions of Sm+ and Sm−
are both P2; and
(4) merging first feature vector Sm+ and the second feature vector Sm−
for each [Gm(i, j)]M×
M as an initial recognition feature vector V=[S+1 S−
1 S+2 S−
2 . . . S+K S−
K ] with a dimension of 2KP2,compressing the features, including compressing the initial recognition feature vector V and obtaining a recognition feature vector Vc with a low dimension of the character image of the character to be recognized;
recognizing the character, including employing a specific classifier to calculate a distance between the recognition feature vector Vc and a category center vector of each character category, selecting a nearest distance from distances and the character category corresponding to the nearest distance, and calculating a character code of the character to be recognized according to a national standard code of the character category; and
(c) repeating step (b) to obtain each character code of each character image.
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Abstract
In a method for character recognition based on Gabor filter group the Gabor filter'"'"'s joint spatial/spatial-frequency localization and capability to efficiently extract characters'"'"' local structural features are employed to extract, from the character image, information of the stroke direction of characters as the recognition information of characters, so as to improve the capability to resist the noises, backgrounds, brightness variances in images and the deformation of characters. Using this information, a simple and effective parameter design method is put forward to optimally design the Gabor filter, ensuring a preferable recognition performance; a corrected Sigmoid function is used to non-linearly adaptively process the stroke direction information output from the Gabor filter group. When extracting the feature from blocks, Gaussian filter array is used to process the positive and negative values output from Gabor filter group to enhance the discrimination ability of the extracted features.
55 Citations
10 Claims
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1. A method for character recognition based on a Gabor filter group, said method comprising:
-
(a) pre-processing a character image, said pre-processing including receiving, pre-processing the character image of a character to be recognized, and obtaining a binary or gray image with N×
N pixels for each character to be recognized, wherein said binary or gray image for each character to be recognized is represented by a matrix [A(i, j)]N×
N;(b) processing the matrix [A(i, j)]N×
N for the character image of each character to be recognized obtained in the step (a), said processing including;extracting stroke direction information of the character to be recognized, said extracting including employing the Gabor filter group which is composed of K two-dimension Gabor filters to extract stroke information in K different directions from the matrix [A(i, j)]N×
N of the character image of the character to be recognized, and obtaining K matrixes [Gm(i, j)]M×
M, m=1 . . . K, of the character image of the character to be recognized, wherein each of said matrixes [Gm(i, j)]M×
M, m=1 . . . K, possesses M×
M pixels and represents the stroke information of one of the K directions;extracting features from blocks, including the steps of; (1) evenly dividing each of said K matrixes [Gm(i, j)]M×
M into P×
P rectangular areas which are overlapped with each other and have a length L for each side of each rectangular are;(2) respectively calculating a first weighted sum of positive values and a second weighted sum of negative values of all pixels within the area, at the center of each rectangular area; (3) forming a first feature vector Sm+ of positive values and a second feature vector Sm−
of the negative values according to the first weighted sum and the second weighted sum of each rectangular area of each matrix, wherein the dimensions of Sm+ and Sm−
are both P2; and(4) merging first feature vector Sm+ and the second feature vector Sm−
for each [Gm(i, j)]M×
M as an initial recognition feature vector V=[S+1 S−
1 S+2 S−
2 . . . S+KS−
K ] with a dimension of 2KP2,compressing the features, including compressing the initial recognition feature vector V and obtaining a recognition feature vector Vc with a low dimension of the character image of the character to be recognized; recognizing the character, including employing a specific classifier to calculate a distance between the recognition feature vector Vc and a category center vector of each character category, selecting a nearest distance from distances and the character category corresponding to the nearest distance, and calculating a character code of the character to be recognized according to a national standard code of the character category; and (c) repeating step (b) to obtain each character code of each character image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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Specification