Handwritten Chinese Character Recognition Using Modified LDA and Kernel FDA

The effectiveness of kernel fisher discrimination analysis (KFDA) has been demonstrated by many pattern recognition applications. However, due to the large size of Gram matrix to be trained, how to use KFDA to solve large vocabulary pattern recognition task such as Chinese Characters recognition is still a challenging problem. In this paper, a two-stage KFDA approach is presented for handwritten Chinese character recognition. In the first stage, a new modified linear discriminant analysis method is developed to get the recognition candidates. In the second stage, KFDA is used to determine the final recognition result. Experiments on 1034 categories of Chinese character from 120 sets of handwriting samples shows that a 3.37% improvement of recognition rate is obtained, which suggests the effectiveness of the proposed method.