| In the fields such as finance, insurance, post and revenue, we need to transformhandwritten Chinese characters into digital information which can be saved in computers so thatthey could be intercommunicated conveniently. And the demand is increasing year by year. Soresearch on off-line handwritten Chinese character has a broad application background.Meanwhile, off-line handwritten Chinese character recognition is a difficulty in theresearch field of pattern recognition, which covers all typical problems in pattern recognitionsuch as feature extraction, classifier selection and sample collection. So there is academic valuein research on it.This paper aims at research of feature extraction on handwritten Chinese characters, andbesides, an experimental off-line handwritten Chinese character recognition system is designedin this paper. In this paper, a novel extraction method is applied, which involved feature fusionof local feature and whole feature. The local feature is extracted based on Gabor transformationand elastic meshes, and the whole feature is extracted based on Zernike moment transformation.Gabor filters have the strong ability of distinguishing different directions. First we can design aset of filters to decompose an image of a Chinese character into four different directional strokesegments, namely horizontal, vertical, right-diagonal, and left-diagonal. After that, elasticmeshes are built from the original image and applied in the four stroke images. Then the pixelsdistribution of every mesh in every image could be calculated and characterized as the localfeature. Being motion, rotation and transformation invariant, Zernike moments can extract thewhole feature of Chinese characters. The fused new features have stronger class separabilitypower because they can offset the disadvantages of the two former ones.The experimental results show that the recognition rate of this method is greatly improvedto over92%, which indicate that the method is effective. |
PDF Full Text Request