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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Guo, F. J., Zhen, L. X., Ge, Y., & Zhang, Y. (2008). An efficient candidate set size reduction method for coarse-classification in Chinese handwriting recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4768 LNCS, pp. 152–160). https://doi.org/10.1007/978-3-540-78199-8_9
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