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

In our previous works, a recognition system named ResifCar was designed specifically for on-line handwritten character recognition. This system is based on an explicit modeling by hierarchical fuzzy rules. Thus, it is understandable an optimizable after the learning stage. We present in this article a new classifier that is an extension of ResifCar. Indeed it tries to combine ResifCar’s advantages with a generic aspect to handle different recognition problems. This new hybrid system combines two complementary levels. The first one uses a robust modeling by an intrinsic fuzzy clustering of each class and determines their confusing areas. The second level, based on fuzzy decision trees, operates a progressive discrimination inside these areas. Both levels are formalized by fuzzy inference systems organized hierarchically and fused for final decision. Experiments were conducted on the one hand on classical benchmarks 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.