An efficient candidate set size reduction method for coarse-classification in Chinese handwriting recognition

Authors:
Feng-Jun Guo;Li-Xin Zhen;Yong Ge;Yun Zhang
Affiliations:
Motorola Labs, China Research Center, Shanghai, P.R.C.;Motorola Labs, China Research Center, Shanghai, P.R.C.;Motorola Labs, China Research Center, Shanghai, P.R.C.;Electronic Engineering Department, Shanghai Jiaotong Univ., Shanghai, P.R.C.
Venue:
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Year:
2006

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Abstract

In this paper, we introduce an efficient clustering based coarse-classifier for a Chinese handwriting recognition system to accelerate the recognition procedure. We define a candidate-cluster-number for each character. The defined number indicates the within-class diversity of a character in the feature space. Based on the candidate-cluster-number of each character, we use a candidate-refining module to reduce the size of the candidate set of the coarse-classifier. Experiments show that the method effectively reduces the output set size of the coarse-classifier, while keeping the same coverage probability of the candidate set. The method has a low computation-complexity.