Patent ID: 7174044

Claim:
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 NÃ—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)] NÃ—N ; (b) processing the matrix [A(i, j)] NÃ—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)] NÃ—N of the character image of the character to be recognized, and obtaining K matrixes [G m (i, j)] MÃ—M , m=1 . . . K, of the character image of the character to be recognized, wherein each of said matrixes [G m (i, j)] MÃ—M , m=1 . . . K, possesses MÃ—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 [G m (i, j)] MÃ—M into PÃ—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 S m + of positive values and a second feature vector S m âˆ’ 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 S m + and S m âˆ’ are both P 2 ; and (4) merging first feature vector S m + and the second feature vector S m âˆ’ for each [G m (i, j)] MÃ—M as an initial recognition feature vector V=[S + 1 S âˆ’ 1 S + 2 S âˆ’ 2 . . . S + K S âˆ’ K ] with a dimension of 2KP 2 , compressing the features, including compressing the initial recognition feature vector V and obtaining a recognition feature vector V c 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.