Abstract

Chinese character recognition systems based on local structure features face difficulties to recognize characters with similar structures. Since moment-based features could capture the global statistic properties of an image rather than local structure features, they are introduced into Chinese character recognition systems. This paper proposed a set of feature vectors based on pseudo-Zernike moments for Chinese character recognition. Three different feature vectors are composed of different parts of four selected lower pseudo-Zernike moments. Experiments on a set of 6,762 Chinese characters show that this method performs well to recognize similar-shaped Chinese characters. The rotational invariant property of pseudo-Zernike moments is also verified.

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