Shape based learning for a multi-template method, and its application to handprinted numeral recognition

Abstract

Character recognition using multi-template methods is promising. Higher classification performance can be achieved according to an increase in the number of templates. However, classification performance is saturated because there is classifiability loss in feature extraction. The paper proposes a new multi-template method which learns training patterns with character shape information assigned by the authors. This method uses contour feature and direction feature, and includes a character shape consistency test applied to the conventional multi-template methods. The paper presents experimental results obtained from handprinted numerals. On the ETL-6 database classification experiment the classification rate was 99.19% and the substitution rate was 0.03%. A higher classification rate could be achieved.