A neural network expert system for Chinese handwriting-based writer identification

Some techniques have been reported for Chinese off-line handwriting-based writer identification and signature verification. Some of them assume that the written text is fixed (e.g. in signature verification), and others presented newly don't need the presupposition since they identify the writer of a piece of handwriting text from the global style. But most of them are not used in practical application and have some disadvantages, which cannot be solved well now. In this paper, we present a system integrating expert system and neural network to identify the writer of Chinese handwriting text. Designed for a practical application of writer identification, this system can perform as well as human expert and exhibit characteristics of a traditional symbolic expert system with high accuracy.