MULTISTAGE PRE-CANDIDATE SELECTION IN HANDWRITTEN CHINESE CHARACTER-RECOGNITION SYSTEMS

Abstract

This paper presents a model for reducing the processing time needed for recognition of handwritten Chinese characters via multi-stage pre-candidate selection. The features used in pre-candidate selection are ordered according to the reduction rates evaluated from a set of training characters. A multi-stage pre-candidate selection module is activated for each input character. In each stage, we use a single feature computed from the input character to eliminate impossible categories of characters. When the number of stages increases, the pre-candidate selection time will increase, while the candidate selection time will decrease. The total execution time will decrease when the increase in pre-candidate selection time is less than the decrease in candidate selection time. The total execution time can be predicted from the set of training characters. The number of stages of the pre-candidate selection module is selected to be that which results in the minimum total execution time. Experiments are performed on the CCL/HCCR1 database with 5401 categories of handwritten Chinese characters. The experimental results show that the proposed model can reduce the total execution time significantly without decreasing the precision of the candidate selection module.