Scene character recognition problem has attracted a great attention in computer vision field. As one of the most widely used characters, Chinese characters are more complicated than European characters, especially when it comes to those characters appeared in various scene texts. The recognition of Chinese scene characters is still far from a satisfactory level owing to the lack of adequate training data and efficient learning methods. In this paper, we propose a new strategy to divide training data and combine multiple classifiers to get a better performance of Chinese scene character recognition. Besides, facing the problem of data shortage, we propose a scene character synthesis method to gain enough training data. By applying the proposed training strategy, the average recognition accuracies of Random Forest (RF) and Support Vector Machine (SVM) have been improved by nearly 22% and 14%, respectively.