Affiliations: [a] School of Computer Science & Engineering, University of New South Wales, 2052 Sydney, Australia | [b] School of Computing, University of Plymouth, Plymouth PL4 8AA, United Kingdom
Abstract: The recognition of Chinese characters has been an area of great interest for many years, and a large number of research papers and reports have already been published in this area. There are several major problems with Chinese character recognition: Chinese characters are distinct and ideographic, the character size is very large and many structurally similar characters exist in the character set. Thus, classification criteria are difficult to generate. This article presents a new technique for the recognition of hand-printed Chinese characters using statistical pattern classification. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct, and difficult to make tolerant to variation in writing styles. The article also discusses Chinese character recognition using dominant point feature extraction, and statistical pattern classification. The system was tested with 500 characters (each character has 40 samples), and the rate of recognition obtained was 84.45%. This strongly supports the usefulness of the proposed measures for Chinese character classification.
Keywords: Chinese characters, Parallel thinning algorithm, Dominant point, Feature extraction, Statistical pattern classification
