HAND-PRINTED NUMERALS RECOGNITION BY LEARNING DISTANCE FUNCTION

In designing more accurate character recognition, revealing the differ ences with other categories in distance function is important. In this paper, I propose Learning by Discriminant Analysis (LDA) as a method to learn distance functions. With a weighted Euclidean distance and a quadratic discriminant function as the original distance functions, LD A learns parameters by superposing the decision function for searching on the pattern set of the noticed category. The results for handwritt en numeral recognition rate improved dramatically and its effectivenes s was verified. In addition, when the values of the parameters after l earning are changed and applied in a weighted Euclidean distance so th at the misread patterns before learning are efficiently segmented and strong correlations exist between features, appropriate category bound aries are obtained. When applied to the quadratic discriminant functio n, the effect of the offset from the normal distribution of features i s reduced.