Hybrid neural networks system for large scale Chinese character set recognition

This paper addresses a hybrid neural networks system, “TsingNeu-1”, for large scale printed Chinese character recognition. The system, which consists of three-level structure of neural networks with feedback error control, is a well designed unbalanced hierarchical tree structure with its functional parts working in a cooperative way according to their functions. Feedback error control based on output evaluation has been adopted for improving robustness of the system. Recognition features of the Chinese characters are extracted automatically and adaptively by self-organizing learning in every stage of the recognition processes. The implemented system can recognize 3755 categories of Chinese characters and some common used punctuations with various fonts and sizes. Experimental results show that the whole system is of reasonable size, easy to train and satisfactory performance