A neural network recognition system for handwritten Chinese character using structured approach
Daniel Yeung, Hak-shun Fong · 2002
In this paper, a neural network based off-line recognition system for handwritten Chinese characters is presented. Seventeen character categories are handled with a recognition rate of 52.44%. Tolerance to shift, slight rotation and slight scaling of the input characters is achieved by the system. The approach demonstrates an integration of neural computation and structural representation of Chinese characters. Neural network is employed for its tolerance to inexactness and noise contamination of input patterns, while structural representation is adopted for its relevance to the construction of Chinese characters. Being a neural network based system, it is adaptable to accommodate newly encountered writing styles of a category. Moreover, an addition of new character categories do not require the removal of the established knowledge in the current system.>