Discrimination between printed and handwritten characters for cheque OCR system

Author

Xu, Wei-ran ; Zhang, Hong-Gang ; Guo, Jun ; Chen, Guang

Author_Institution

Beijing Univ. of Posts & Telecommun., China

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1048

Abstract

The identification of printed and handwritten characters is a fundamental and important issue for the cheque OCR system to achieve high-accuracy. In this paper, a novel method is presented to identify the written type based on only 4 or 5 characters in a severely corrupted bank cheque image. We first extract 4 kinds of features, totaling 17 features. Then the most suitable features are selected using the method based on separability measure. Finally, the selected features are used by a naive Bayesian classifier to realize the discrimination. Using 12,158 real checks to test our method, the accuracy is 99.2%.

Keywords

Bayes methods; bank data processing; feature extraction; handwritten character recognition; image classification; optical character recognition; OCR system; bank cheque; feature extraction; font recognition; handwritten characters recognition; naive Bayesian classifier; printed characters recognition; separability measure; Banking; Bayesian methods; Character recognition; Handwriting recognition; Image recognition; Optical character recognition software; Pixel; Seals; Telecommunications; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on

Print_ISBN

0-7803-7508-4

Type

conf

DOI

10.1109/ICMLC.2002.1174543

Filename

1174543