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The accuracy of handwritten Chinese character recognition can be improved by pair discrimination of similar characters. In this paper, we propose a new method for combining the baseline classifier with incomplete pair discriminators to better exploit their complementariness. The outputs of the baseline classifier and pair discriminators are transformed to two-class probabilities, which are then fused by pairwise coupling (PWC) for final decision. In our experiments using the modified quadratic discriminant function (MQDF) as baseline classifier and LDA-based pair discriminators, the PWC method outperforms the filter method. At best, the error rate of MQDF was reduced by factors over 28%.