Chung Nak Woo, Kim Byung Gi

The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 4, No. 2, pp. 401-408, Feb. 1997
10.3745/KIPSTE.1997.4.2.401,   PDF Download:

Abstract

Human gets almost all of his knowledge from the recognition and the accumulation of input patterns, image or sound, that he gets through his eyes and through his car. Among these means, his character recognition, an ability that allows him to recognize characters and understand their meanings through visual information, is now applied to a pattern recognition system using neural network in computer. Recurrent neural network is one of those models that reuse the output value in neural network learning. Recently many studies try to apply this recurrent neural network to the classification of static patterns like off-line handwritten characters. But most of their efforts are not so effective until now. This study suggests a new type of recurrent neural network for an effective classification of the static patterns such as off-line handwritten characters. Using the new J-E(Jordan-Elman) neural network model that enlarges and combines Jordan Model and Elman Model, this paper shows that this new type is better than those of before in recognizing the static patterns such as figures and handwritten characters.

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