Preclassification for Handwritten Chinese Character Recognition Using Fuzzy Rules and SEART Neural Net

Abstract

In this paper, a method of character preclassification for handwritten Chinese character recognition is proposed. Since the number of Chinese characters is very large (at least 5401s for daily use), we employ two stages to reduce the candidates of input character. In stage I, we try to extract the first primitive features from handwritten Chinese characters and use the fuzzy rules to create the four preclassification groups. The purpose in stage I is to reduce the candidates roughly. In stage II, we extract the second primitive features from handwritten Chinese characters and then use the Supervised Extended ART (SEART) as the classifier to generate the preclassifcation classes for each preclassification group that we create in stage I. Since the number of characters in each preclassification class is smaller than that in the whole character set, the problem becomes simpler. In order to evaluate the problem becomes simpler. In order to evaluate the proposed preclassification system, we use the 605 Chinese character categories in the text books of elementary school as our training and testing data. The database used is HCCRBASE (provided by CCL, ITRI, Taiwan). We select the even samples of samples 1-100 as the training set, and the odd samples of them as the testing set. The preclassification rate that characters of testing set can be distributed into correct preclassification classes is 98.11%.