High Speed Coarse Classification for Large Character Set Using A Variable Candidate Selection Method

摘要

This paper describes a high-speed coarse classifier, which makes use of a variable candidate selection method. The classifier is applicable to large character set recognition, such as Chinese, Japanese character. In designing the classifier, three strategies are used: lookup table, dimension reduction, and variable number of candidate selection. The classifier points to two directions: speeding up candidate selection and reduce the candidate set as much as possible. Compared with the fixed number candidate selection method, the third strategy can reduce the average candidate length significantly. In addition, we proposed an adaptively threshold estimating algorithm using distance histogram. The performance of this coarse classifier was test on the 863 Testing System. Experimental results verified its affectivity.