Support vector machine and its application in handwritten numeral recognition

Author

Bin, Zhao ; Yong, Liu ; Shao-wei, Xia

Author_Institution

Dept. of Autom., Tsinghua Univ., Beijing, China

Volume

2

fYear

2000

fDate

6/22/1905 12:00:00 AM

Firstpage

720

Abstract

The support vector machine (SVM) is a new learning machine with very good generalization ability, which has been applied widely in pattern recognition and regression estimation. Four practical handwritten numeral SVM classifiers are proposed in this paper, which has been utilized successfully in Chinese check recognition system. The experiment on NIST numeral database and the actual check samples show that compared with other classifiers, SVM possesses better generalization ability

Keywords

bank data processing; generalisation (artificial intelligence); handwritten character recognition; image classification; learning automata; statistical analysis; Chinese check recognition system; NIST numeral database; cheque recognition system; generalization ability; handwritten numeral SVM classifiers; handwritten numeral recognition; pattern recognition; regression estimation; support vector machine; Automation; Handwriting recognition; Input variables; Lagrangian functions; Machine learning; Pattern recognition; Quadratic programming; Risk management; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2000. Proceedings. 15th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-0750-6

Type

conf

DOI

10.1109/ICPR.2000.906176

Filename

906176