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Recognition of handwritten similar Chinese characters by neural networks

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2 Author(s)
Hsin-Chia Fu ; Dept. of Comput. Sci. & Inf. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan ; Chen, J.M.

This paper presents a multi-stage neural networks for the recognition of similar Chinese characters. In this research, the authors have developed a three stage recognition structure: 1) an overlapped c-means clustering algorithm to implement a coarse classifier; 2) a Bayesian decision based neural network as a fine classifier; and 3) a two-layered feedforward neural network for similar character recognition. A personal computer based prototype recognition system has been built. By using the CCL/NCCR1 database (5401 characters×200 samples) as a benchmark, the training and testing results show that the proposed prototype system achieves some improvement on the efficiency (recognition time of 0.885 second per character on a Pentium-90 based PC) and robustness (recognition rate of 90.12% without any rejection, and 94.11% with 6.7% of rejection, respectively)

Published in:

Neural Networks for Signal Processing [1996] VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop

Date of Conference:

4-6 Sep 1996