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.