An effective result-feedback neural algorithm for handwritten character recognition

Authors:
Xiaoyan Zhu;Yu Hao;Yifan Shi;David Zhang
Affiliations:
State Key Lab of Intelligent Technology and Systems, Department of Computer science, Tsinghua University, Beijing, China;State Key Lab of Intelligent Technology and Systems, Department of Computer science, Tsinghua University, Beijing, China;State Key Lab of Intelligent Technology and Systems, Department of Computer science, Tsinghua University, Beijing, China;Department of Computing, Hong Kong Polytechnic University Kowloon, Hong Kong
Venue:
Neural, Parallel & Scientific Computations
Year:
2001

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Abstract

In this paper, a new algorithm of handwritten character recognition based on result-feedback is proposed. It is designed as an effective neural network by adding confidence back-propagation and input modification, thus both pre-processing and recognition operations are closely integrated together. The convergence of the algorithm is proved and many experiments show that the error rate in such a result-feedback neural network (RFNN) can be greatly reduced as well as the robust to environmental noise.