Handwritten Character Recognition Based on Parallel Feature Combination and Generalized K-L Expansion
基于并行特征组合与广义K-L变换的字符识别

Considering the weaknesses of traditional serial feature fusion technique, a novel parallel features fusion method is proposed in this paper. The main idea of this method can be described as follows. First of all, two sets of feature vectors corresponding to a same sample space are combined together via complex vectors, which are used to construct a complex feature vector space. Then, the classical K-L transform and K-L expansion methods are developed theoretically to suit for feature extraction in the complex feature space. Moreover, the symmetric property of parallel feature fusion is revealed, and, how to combine features effectively is discussed in detail. Finally, the proposed method is used to solve the handwritten character feature extraction and recognition problems. Experiments are performed on NUST603 handwritten Chinese character database built in Nanjing University of Science and Technology as well as the well-known CENPARMI handwritten digit database of Concordia University. The experimental results indicate that the recognition rates are improved significantly after parallel feature fusion, and the proposed parallel features fusion method is superior to the traditional serial feature fusion one.