A hybrid model for handwritten Chinese characters recognition

Author

Hong-De Chang ; Jhing-Fa Wang ; Shye-Chorng Kuo

Author_Institution

Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan

Volume

4

fYear

1993

fDate

19-21 Oct. 1993

Firstpage

494

Abstract

In this paper, a hybrid model is presented for handwritten Chinese characters recognition. First, a new and reliable stroke extraction method is proposed. Based on the extracted strokes, the cross-points can also be determined. The characters with the same numbers of strokes and cross-points are grouped together in rough classification stage. Second, a Bayesian neural network for fine classification is presented. The experimental results show that the commonly used 1000 characters can be partitioned into 70 groups and each group contains 23 characters on the average and the average recognition rates of 95% and 85% for recognizing the characters written by two specified persons and selected from the CCL/HCCR1 database had been obtained.<>

Keywords

Bayes methods; handwriting recognition; image classification; neural nets; Bayesian neural network; CCL/HCCR1 database; cross-points; handwritten Chinese characters recognition; hybrid model; rough classification; stroke extraction; Bayesian methods; Character recognition; Data mining; Databases; Neural networks; Reliability engineering; Shape; Skeleton; Symmetric matrices; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on

Conference_Location

Beijing, China

Print_ISBN

0-7803-1233-3

Type

conf

DOI

10.1109/TENCON.1993.320539

Filename

320539