Match between normalization schemes and feature sets for handwritten Chinese character recognition

Because of the large number of Chinese characters and many different writing styles involved, the recognition of handwritten Chinese characters remains a very challenging task. It is well recognized that a good feature set plays a key role in a successful recognition system. Shape normalization is as well an essential step toward achieving translation, scale, and rotation invariance in recognition. Many shape normalization methods and different feature sets have been proposed in the literature. We first review five commonly used shape normalization schemes and then discuss various feature extraction techniques usually used in handwritten Chinese character recognition. Based on numerous experiments conducted on 3,755 handwritten Chinese characters (GB2312-80), we discuss the matches made between the normalization schemes and the feature sets and suggest the best match between them in terms of classification performance. The nearest neighbor classifier was adopted in our experiments with templates obtained by using the K-means clustering algorithm