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

Authors:
Nicolas Ragot;Eric Anquetil
Affiliations:
-;-
Venue:
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Year:
2003

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Abstract

In our previous works, a recognition system named ResifCarwas designed specifically for on-line handwrittencharacter recognition. This system is based on an explicitmodeling by hierarchical fuzzy rules. Thus, it is understandablean optimizable after the learning stage. We present inthis article a new classifier that is an extension of ResifCar.Indeed it tries to combine ResifCar's advantages witha generic aspect to handle different recognition problems.This new hybrid system combines two complementary levels.The first one uses a robust modeling by an intrinsicfuzzy clustering of each class and determines their confusingareas. The second level, based on fuzzy decision trees,operates a progressive discrimination inside these areas.Both levels are formalized by fuzzy inference systems organizedhierarchically and fused for final decision. Experimentswere conducted on the one hand on classical benchmarksand on the other hand on on-line handwritten digitsand lower-case letters. For all of these cases, the classifierachieves good recognition rates without final optimization.