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

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 c...

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Bibliographic Details
Published in:2007 IEEE International Conference on Robotics and Biomimetics pp. 1344 - 1349
Main Authors: Da Lu, Qiwei Chen, Wei Pu, Mingpei Xie
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2007
Subjects:
artificial neural network
Character recognition
Feature extraction
fuzzy matching algorithm
Fuzzy neural networks
Fuzzy sets
Handwriting recognition
handwritten Chinese character preclassification
Neural networks
Pattern recognition
Probability
Prototypes
Subspace constraints
supervised extended ART
ISBN:1424417619, 9781424417612
Online Access:Get full text
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Summary: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%.
ISBN:1424417619
9781424417612
DOI:10.1109/ROBIO.2007.4522359