Abstract

Abstract — Moment descriptors have long been applied in object recognition since the early years of the development of the moment theories. Nowadays, discrete orthogonal moments have been studied and proposed for they are superior to traditional continuous ones. In this paper, a set of moment features extracted from the discrete Tchebichef moments for Chinese character recognition is presented. A new method of evaluating the variance values of each moment feature is applied in this research. Tested on a set of 6,763 Chinese characters, our newly proposed Tchebichef moment features perform very well in distinguishing all Chinese character pairs that have similar structures. Index Terms — Discrete orthogonal moments, tchebichef moments, Chinese character recognition. I. I NTRODUCTION Since Hu [1] introduced the moment methods in 1961, moment descriptors have been widely used in image representation, pattern recognition, and object classification. One of the applications using moment features is the Chinese character recognition. Chinese characters are very different from many other languages in conveying information, while the structure of a character is a key to its meaning and pronunciation. Many feature extraction methods applied in the Chinese character recognition systems are based on the local features, such as strokes and feature points [2]-[5]. While the existing methods are quite efficient in general, there are some difficulties to distinguish two characters when they have very close structures. On the other hand, the moment method has the advantage of utilizing the global features of the Chinese characters, therefore, it can be used as a complementary scheme to overcome the obstacles confronted by other systems. Some Chinese character recognition systems based on the orthogonal moment descriptors have been reported [6]-[8]. Recently, the emergence of discrete orthogonal moments has substantially enriched the moment methods. Moments based on Tchebichef and Krawtchouk polynomials were introduced by Mukundan

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