Abstract
There has been much research in recent decades on character recognition methods, and some methods have already been put into practical use. There are many unresolved problems, however, with respect to handwritten character recognition as composed with printed character recognition. The authors considered discriminant functions, which constitute the most important part of a character recognition method. As a result of considering problems of conventional statistical discriminant functions, the authors propose applying the fuzzy theory to discriminant functions. The soβcalled fuzzy discriminant function is capable of representing a data distribution in a more flexible manner because it consists of membership functions on the principal axes of learning samples.
The authors conducted recognition experiments for handwritten characters with two types of membership functions. In one type the membership values are directly tuned based on human experiences; in the other they are derived from histograms or statistical data. With the former membership function, the recognition rate of 99.0 percent is achieved for [numeric] characters from the handwritten alphanumeric data base ETL6, and with the latter, the rate of 96.0 percent for [hiragana] characters from handwritten educational [kanji] data base ETL8. This result proves the effectiveness of the fuzzy discriminant function. It also indicates that a dynamic combination of human experiences and statistical techniques is a key to practical systems.