A novel multistage classification strategy for handwriting Chinese character recognition using local linear discriminant analysis

In this paper we present a novel multistage classification strategy for handwriting Chinese character recognition. In training phase, we search for the most representative prototypes and divide the whole class set into several groups using prototype-based clustering. These groups are extended by nearest-neighbor rule and their centroids are used for coarse classification. In each group, we extract the most discriminative feature by local linear discriminant analysis and design the local classifier. The above-mentioned prototypes and centroids are optimized by a hierarchical learning vector quantization. In recognition phase, we first find the nearest group of the unknown sample, and then get the desired class label through the local classifier. Experiments have been implemented on CA-SIA database and the results show that the proposed method reaches a reasonable tradeoff between efficiency and accuracy.