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Title: Chinese character classification using an adaptive resonance network
Authors: Gan, K.W. 
Lua, K.T. 
Keywords: Adaptive Resonance Theory
Artificial neural network
Chinese character classification
Unsupervised learning
Issue Date: Aug-1992
Citation: Gan, K.W.,Lua, K.T. (1992-08). Chinese character classification using an adaptive resonance network. Pattern Recognition 25 (8) : 877-882. ScholarBank@NUS Repository.
Abstract: The ability to see through noise and distortion to a pattern is vital to the task of character recognition. Artificial neural networks exhibit such a capability as they are able to generalize automatically once they are trained. An application of an artificial neural network model, the Adaptive Resonance Theory (ART), to Chinese character classification is described. The ART classifier is used to classify 3755 Chinese characters. Our experimental results indicate that the classifier is able to achieve a high classification rate. © 1992.
Source Title: Pattern Recognition
ISSN: 00313203
Appears in Collections:Staff Publications

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