Hand-printed Chinese character recognition via machine learning

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 a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to generate. This paper presents a new technique for the recognition of hand-printed Chinese characters using machine learning C4.5. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The paper also discusses Chinese character recognition using dominant point feature extraction and C4.5. The system was tested with 900 characters (each character has 40 samples) and the rate of recognition obtained was 84%.