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On-Line Character Analysis and Recognition With Fuzzy Neural Networks

Abstract A new recognition system based on a neuro-fuzzy system, called FasArt, is proposed in this paper. Satisfactory results were obtained using the train_r01_v02 UNIPEN dataset, together with a comparison with the recognition rates achieved by independent human testers. Two methods for segmenting handwritten components into strokes are proposed, with better experimental results for the method based on biological models of handwriting, in terms of consistency and network complexity. A systematic experimental study of different codification schemes is also described, based on Shannon entropy and clustering maps. Finally, some steps towards the construction of an allograph lexicon are shown, that exploit the generation of fuzzy-rules by FasArt architecture.