Study on preclassification for handwritten Chinese character based on neural net and fuzzy matching algorithm

Abstract

To settle the recognition task of handwritten Chinese characters, the authors put forward a method for handwritten Chinese character preclassification before character recognition. In this method, Neocognitron was used in extracting stroke features, then uses the Supervised Extended ART (SEART) to create some preclassification groups, and uses matching algorithm of fuzzy prototypes of similarity measurement for character preclassification. The experiment shows this method is effective when used for handwritten Chinese character classification and characters of the testing set can be distributed into correct preclassification classes at a rate of 98.22%.

DOI: 10.1109/ROBIO.2007.4522359

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Cite this paper

@article{Lu2007StudyOP, title={Study on preclassification for handwritten Chinese character based on neural net and fuzzy matching algorithm}, author={Da Lu and Qiwei Chen and Wei Pu and Mingpei Xie}, journal={2007 IEEE International Conference on Robotics and Biomimetics (ROBIO)}, year={2007}, pages={1344-1349} }