High Accuracy Recognition of ETL9B Using Exclusive Learning Neural Network - (ELNET-)

Kazuki SARUTA  Nei KATO  Masato ABE  Yoshiaki NEMOTO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E79-D   No.5   pp.516-522
Publication Date: 1996/05/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Character Recognition and Document Understanding)
Category: Neural Networks
Keyword: 
handwritten character recognition,  neural networks,  ETL9B,  ELNET-,  

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Summary: 
In earlier works we proposed the Exclusive Learning neural NET work (ELNET), which can be utilized to construct large scale recognition system for Chinese characters. However, this did not resolve the problem of how to use training samples effectively to generate more suitable recognition boundaries. In this paper, we propose ELNET- wherein an attempt has been made to deal with this problem. In comparison with ELNET, selection method of training samples is improved. And the number of module size are variable according to the number of training samples for each module. In recognition experiment for ETL9B (3036 categories) using ELNET-, we obtained a recognition rate of 95.84% as maximum recognition rate. This is the first time that such a high recognition rate has been obtained by neural networks.