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Traditional character recognition systems use a single classifier to determine the true class of a given character. However, by using classifiers of different types simultaneously, classification accuracy could be improved. In this paper, we propose a new approach based on majority vote and statistics to support a combined decision among multiple classifiers. First, we find the strengths and weaknesses of all classifiers through the analysis among test characters, templates and classifiers. Then we devise a combination method that can improve classification performance. Experimental results show the effectiveness of our approach.