Recognition of handwritten Chinese characters by multi-stage neural network classifiers

Author

Fu, Hsin-Chia ; Chiang, Kuo-Ping

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan

Volume

4

fYear

1995

fDate

Nov/Dec 1995

Firstpage

2149

Abstract

This paper presents a multi-stage neural network classifiers for handwritten Chinese character recognition. In the proposed system, the authors have developed: (1) a two stage recognition structure: (a) an overlapped c-means clustering algorithm to implement a coarse classifier, (b) a Bayesian decision based neural network as a fine classifier, (2) feature selection and reduction methods, (3) a recognition system on a personal computer, which requires only 3.98 MB RAM for feature vectors storage. By using a large database (5401 characters×100 samples), the training and testing results show the efficiency (recognition time: 0.885 second per character on a Pentium based PC) and robustness (recognition rate: 86.68% and 93.60% of top one and top three respectively) of the proposed system

Keywords

feature extraction; image classification; multilayer perceptrons; optical character recognition; Bayesian decision based neural network; coarse classifier; feature reduction; feature selection; fine classifier; handwritten Chinese characters; multi-stage neural network classifiers; overlapped c-means clustering algorithm; two stage recognition structure; Bayesian methods; Character recognition; Clustering algorithms; Handwriting recognition; Microcomputers; Neural networks; Read-write memory; Robustness; Spatial databases; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.489011

Filename

489011