A Generic Hybrid Classifier Based on Hierarchical Fuzzy Modeling: Experiments on On-Line Handwritten Character Recognition

Abstract : In our previous works, a recognition system named Re- sifCar was designed specifically for on-line handwritten character recognition. This system is based on an explicit modeling by hierarchical fuzzy rules. Thus, it is understand- able an optimizable after the learning stage. We present in this article a new classifier that is an extension of Resif- Car. Indeed it tries to combine ResifCar’s advantages with a generic aspect to handle different recognition problems. This new hybrid system combines two complementary lev- els. The first one uses a robust modeling by an intrinsic fuzzy clustering of each class and determines their confus- ing areas. The second level, based on fuzzy decision trees, operates a progressive discrimination inside these areas. Both levels are formalized by fuzzy inference systems or- ganized hierarchically and fused for final decision. Experi- ments were conducted on the one hand on classical bench- marks and on the other hand on on-line handwritten digits and lower-case letters. For all of these cases, the classifier achieves good recognition rates without final optimization.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01191727
Contributor : Nicolas Ragot <>
Submitted on : Wednesday, September 2, 2015 - 2:08:37 PM
Last modification on : Tuesday, July 2, 2019 - 4:02:03 PM

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  • HAL Id : hal-01191727, version 1

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Nicolas Ragot, Eric Anquetil. A Generic Hybrid Classifier Based on Hierarchical Fuzzy Modeling: Experiments on On-Line Handwritten Character Recognition. 7th International Conference on Document Analysis and Recognition (ICDAR'03), Aug 2003, Edinburgh, United Kingdom. pp.963-967. ⟨hal-01191727⟩

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