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A New Feature Optimization Method Based on Two-Directional 2DLDA for Handwritten Chinese Character Recognition

LDA transformation is one of the popular feature dimension reduction techniques for the feature extraction in most handwritten Chinese characters recognition systems. The integration of the feature extraction and LDA transformation can be viewed as a two-directional feature transformation procedure, one is the pixel-level feature transformation by the summing up or blurring, another is by the LDA matrix, and the transformation coefficients are set empirically in the former. In this paper, we proposed a feature optimization method based on the gradient feature extraction by using the two-directional 2DLDA, which can find the optimal transformation coefficients in two directions. A series of experiments on the randomly selected 15 groups of the similar Chinese character samples from HCL2000 have indicated that, our method can effectively improve the recognition performance, the error rate reduction reaches 45.02% comparing to the traditional method, showing the effectiveness of the proposed approach.