High accuracy handwritten Chinese character recognition by improved feature matching method

Abstract

Proposes some strategies to improve the recognition performance of a feature matching method for handwritten Chinese character recognition (HCCR). Favorable modifications are given to all stages throughout the recognition. In pre-processing, we devised a modified nonlinear normalization algorithm and a connectivity-preserving smoothing algorithm. For feature extraction, an efficient directional decomposition algorithm and a systematic approach to design a blurring mask are presented. Finally, a modified LVQ3 algorithm is applied to optimize the reference vectors for classification. The integrated effect of these strategies significantly improves the recognition performance. Recognition results on the large-vocabulary databases ETL8B2 and ETL9B are promising.