SemiBoost-based Arabic character recognition method

Bing Su, Liangrui Peng, Xiaoqing Ding

Abstract: A SemiBoost-based character recognition method is introduced in order to incorporate the information of unlabeled practical samples in training stage. One of the key problems in semi-supervised learning is the criteria of unlabeled sample selection. In this paper, a criteria based on pair-wise sample similarity is adopted to guide the SemiBoost learning process. At each time of iteration, unlabeled examples are selected and assigned labels. The selected samples are used along with the original labeled samples to train a new classifier. The trained classifiers are integrated to make the final classfier. An empirical study on several Arabic similar character pairs with different similarities shows that the proposed method improves the performance as unlabeled samples reveal the distribution of practical samples.