A new neuro-fuzzy system for logical labeling of documents
Gregorio Ismael Sainz-Palmero, J.M. Izquierdo, Yannis A. Dimitriadis, Juan López Coronado · 1996
Logical labeling, i.e. matching of the physical and logical structure, is an essential part of any document processing system. Contrary to the classical rule-based approach, where inflexible, heuristic knowledge is involved, a new neuro-fuzzy system is proposed in this paper. The main module, called FasArt (fuzzy adaptive system ART-based), is based on the well-known supervised neural architecture fuzzy ARTMAP. This new proposed architecture maintains the positive characteristics of its predecessor, such as capacity for incremental learning, respect to the plasticity-stability dilemma and generation of a symbolic representation of the system performance. Additionally, FasArt has a consistent formulation as a fuzzy logic system, a proven ability for approximation of any continuous function and a parameter that permits us to regulate its fuzziness degree. This new architecture is combined with a module that implements the Viterbi algorithm with the statistical properties of a certain document class, in order to resolve ambiguities or inconsistencies of the classification results provided by FasArt. Experimental results are shown for a variety of business letters, within a system of automatic feeding of an institution mailing database FasArt shows better results than Fuzzy ARTMAP while the use of the Viterbi-based modules boosts the correct labeling results to a 95% from the initial 80%.