Information distribution of the central projection method for Chinese character recognition

A new method called central projection transformation is proposed in this paper for feature extraction. From our experiments, the new method is found to be efficient in extracting features from Chinese characters, which contain a vast amount of information. Chinese characters have complex structures, and some of them are composed of several separate components, so several contours are embedded in a character. This may obstruct application of the contour approach to recognizing Chinese characters. Central projection transformation can convert such a multicontour pattern into a solid convex pattern, whose contour is a unique polygon. Most of the information of this new pattern is still located around its peripheries. In this paper, information contents and entropy measurements are studied in both original Chinese characters and transformed new objects from the 3500 most frequently used Chinese characters. The results indicate that both the information contents and entropy measurements of pixels vary according to the positions of the points, and that most of the information is located around the peripheries of the original characters as well as of the new ones. This approach can greatly simplify the processing of Chinese characters and other multicontour patterns. It is also a powerful tool for processing Arabic characters, Japanese characters and other characters.