Study of Off-Line Handwritten Chinese Character Recognition Based on Dynamic Pruned Binary Tree SVMs

Abstract

The Chinese off-line handwritten recognition is a multi-class classification problem. Since there exits certain relation between some classes, it is unnecessary to differentiate all types of the Chinese characters at the time of identifying one type of Chinese character. In this paper, an improved dynamic pruned binary tree SVM multi-classification algorithm is presents. Unvalued support vector is removed at each identification, then a new binary tree is reconstructed according to the features of the structure of the Chinese character, The number of the support vector machines is reduced and the identification speed is increased. Compared with different multi-class classification algorithms, the simulation results show that the method can improve the speed of identifying the classes of the Chinese characters. It is effective and accuracy high.