Handwritten Numeral Recognition Using Personal Handwriting Characteristics Based On Clustering Method

Abstract

To improve recognition rate, it is important not only to utilize one character feature but personal handwriting characteristics. This paper realizes above approach based on our investigation result that characters written by the same writer have similar shapes and that there are several shapes even in the same category. In our method, clustering method is used to absorb the variance of character shapes in the category. First, character recognition for each character is executed. Next, misrecognized character candidates are extracted as isolated cluster by within-category clustering. Then, recognition results of the extracted characters are amended by between-category clustering which evaluates the distance between the cluster composed of misrecognized characters and the cluster composed of correctly recognized characters in every categories. Finally, experimental results shows that recognition rate is remarkably improved by our method.