New features for Chinese character recognition

Abstract

The wide range of shape variations for Chinese characters requires an adequate representation of the discriminating features for classification. For the recognition of Latin characters or numerals pixel values of a normalized raster image are proper features to reach very good recognition rates. But Chinese characters require a much higher resolution of the normalized raster image to enable a discrimination of complex shaped characters which leads to a feature space dimensionality of prohibitive computational effort for classification. Therefore feature extraction algorithms are needed which capture the discriminative characteristics of character shapes in a compact form. Several algorithms were proposed in the past and many of them are based on the contour data. This paper also introduces a contour based approach which is very time efficient and overcomes the problem of vanishing lines during anisotropic size normalization.