Pairwise classifier combination and its application on Chinese character recognition

The complexity of pattern recognition increases along with the number of classes to be classified. As a result, large-scale pattern recognition problems such as Chinese character recognition are very difficult. On the other hand, with least number of classes, the research of pairwise classification has well developed theories and applicable methods. Thus, it is very practical to apply pairwise methods to solve multi-class and large-scale pattern recognition problems. In this paper, the multi-class problem has been broken down into a combination of pairwise problems so that multi-class problem can be solved through pairwise classifier combination. The method has been applied in Chinese character recognition. Comparing with primary system, the first candidate recognition rate increases from 89.25% to 97.94%; the error rate is cut by 80.84%.