Rejection Optimization Based on Threshold Mapping for Offline Handwritten Chinese Character Recognition

In this paper, a rejection optimization method based on rejection threshold mapping is proposed. Different from conventional rejection methods which use the same rejection threshold for all samples, this technique utilizes the local information of samples to optimize the rejection threshold. The samples with the same class pair in the first two recognition candidates are treated as the same sample category. Based on the rejection distribution of the sample categories, the parameters of rejection threshold mapping of similar character pairs are learned and stored. When performing rejection, the corresponding mapping parameters are searched according to the class pair in the first two recognition candidates, and applied on the input threshold. The transformed threshold is used in final rejection decision. The experiments show that it is able to decrease error rate under same rejection rate on handwritten Chinese character recognition which verify its effectiveness.