Abstract
In a recognition system of off-line handwritten Chinese characters, which has a proper recognition rate, improving the recognition rate of similar characters is the key to raising the whole recognition rate. K-L transformation, linear projection, and nonlinear projection are used to visualize the distribution of high-dimension Chinese character vectors. By making comparison experiments between very-similar and very-different Chinese characters, we summarize the distribution characteristic of the high-dimension similar Chinese characters. Utilizing the Mahalanobis distance to measure the similarity of characters and according to the results of statistical experiments, we present a learning algorithm to determine the similar Chinese characters' boundary based on unequal-contraction of dimension.
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