DocumentCode :
288742
Title :
Dynamics-based active learning for handwritten character recognition
Author :
Natori, Naotako ; Nishimura, Kazuo
Author_Institution :
70, Yanagi-cho, Saiwai-ku, Kawasaki-shi, Kanagawa, Japan
Volume :
5
fYear :
1994
fDate :
27 Jun-2 Jul 1994
Firstpage :
2875
Abstract :
This paper proposes a new efficient learning of a neural network for handwritten character recognition. Like human learning, the proposed learning acquires excellent recognition ability for unknown character patterns only from a small number of typical character patterns. The proposed learning is based on the dynamics of a human´s hand mechanism and has been realized on a neural network. The recognition rates exceed those by a conventional statistical method
Keywords :
Biological neural networks; Biology computing; Character recognition; Handwriting recognition; Humans; Iterative algorithms; Laboratories; Neural networks; Pattern recognition; Statistical analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
Type :
conf
DOI :
10.1109/ICNN.1994.374687
Filename :
374687
Link To Document :
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