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Hybrid Statistical-Structural On-line Chinese Character Recognition with Fuzzy Inference System

Adrien Delaye 1 Sébastien Macé 1 Eric Anquetil 1 
1 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 : In this paper, we propose an original hybrid statistical-structural method for on-line Chinese character recognition. We model characters thanks to fuzzy inference rules combining morphological and contextual information formalized in a homogeneous way. For that purpose, we define a set of primitives modeling all the stroke classes that can be found in handwritten Chinese characters. Thus, each analyzed stroke can be classified as primitive without any segmentation process. Inference rules are built from the coupling of a priori information about the primitives constituting the characters and automatic modeling of their relative positioning. The fuzzy inference system aggregates these rules for decision making. First experiments validate this method with a recognition rate of 97.5% on a subset of Chinese characters.
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Submitted on : Friday, September 19, 2008 - 10:44:09 AM
Last modification on : Thursday, January 7, 2021 - 4:34:10 PM
Long-term archiving on: : Thursday, June 3, 2010 - 9:48:05 PM


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  • HAL Id : inria-00322952, version 1


Adrien Delaye, Sébastien Macé, Eric Anquetil. Hybrid Statistical-Structural On-line Chinese Character Recognition with Fuzzy Inference System. International Conference on Pattern Recognition, IAPR, Dec 2008, Tampa, Florida, United States. ⟨inria-00322952⟩



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