Abstract
A new document management system is proposed in this paper. Its kernel is based on a new set of neuro-fuzzy systems of the ART family: FasArt and RFasArt. The first one, FasArt, is used to support a simple Optical Character Recognition (OCR) that inherits fine properties of ART architectures, such as fast and incremental learning, stability and modularity. On the other hand, RFasArt is a new recurrent version of FasArt which efficiently exploits contextual information in the task of logical labeling. The proposed system is extensively tested in two real-world applications, i.e. E-mail of printed business letter and digital library of scientific papers. Experimental results show logical labeling and OCR rates over 90%. The proposed system is better compared to a previous system proposed by the group, where instead of using contextual information in an integrated way, a postprocessing Viterbi-based model was employed.
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