Special Radical Detection by Statistical Classification for On-line Handwritten Chinese Character Recognition

Ma Long-Long 1 Adrien Delaye 2 Cheng-Lin Liu 1
2 IMADOC - Interprétation et Reconnaissance d’Images et de Documents
UR1 - Université de Rennes 1, INSA Rennes - Institut National des Sciences Appliquées - Rennes, CNRS - Centre National de la Recherche Scientifique : UMR6074
Abstract : The hierarchical nature of Chinese characters has inspired radical-based recognition, but radical segmentation from characters remains a challenge. We previously proposed a radical-based approach for on-line handwritten Chinese character recognition, which incorporates character structure knowledge into integrated radical segmentation and recognition, and performs well on characters of left-right and up-down structures (non-special structures). In this paper, we propose a statistical-classification-based method for detecting special radicals from special-structure characters. We design 19 binary classifiers for classifying candidate radicals (groups of strokes) hypothesized from the input character. Characters with special radicals detected are recognized using special-structure models, while those without special radicals are recognized using the models for non-special structures. We applied the recognition framework to 6,763 character classes, and achieved promising recognition performance in experiments.
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Ma Long-Long, Adrien Delaye, Cheng-Lin Liu. Special Radical Detection by Statistical Classification for On-line Handwritten Chinese Character Recognition. International Conference on Frontiers in Handwriting Recognition, IAPR, Nov 2010, Kolkata, India. ⟨inria-00540548⟩

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