A radical-partitioned coded block adaptive neural network structure for large-volume Chinese characters recognition

Author

Kuo, J.B. ; Mao, M.W.

Author_Institution

Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan

Volume

3

fYear

1992

fDate

7-11 Jun 1992

Firstpage

597

Abstract

A coded block adaptive neural network system using a radical-partitioned structure for a large-volume Chinese character recognition VLSI is presented. Using this coded block adaptive neural network system, 1000 frequently used Chinese characters have been successfully trained in 139.2 h using a 18-MIPS computer. Based on the simulation results, the coded block system with a radical-partitioned structure provided an acceptable learning time, a good recognition rate, and an excellent expansion capability for large-volume Chinese character recognition using a VLSI

Keywords

VLSI; character recognition; learning (artificial intelligence); neural nets; VLSI; large-volume Chinese characters recognition; learning time; radical-partitioned coded block adaptive neural network structure; recognition rate; Adaptive arrays; Adaptive systems; Character recognition; Computational modeling; Computer networks; Convergence; Neural networks; Neurons; Pattern recognition; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1992. IJCNN., International Joint Conference on

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-0559-0

Type

conf

DOI

10.1109/IJCNN.1992.227109

Filename

227109