Title :
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
fDate :
6/22/1905 12:00:00 AM
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;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.906176
