Hybrid fuzzy-neural systems in handwritten word recognition

Two hybrid fuzzy neural systems are developed and applied to handwritten word recognition. The word recognition system requires a module that assigns character class membership values to segments of images of handwritten words, The module must accurately represent ambiguities between character classes and assign low membership values to a wide variety of noncharacter segments resulting from erroneous segmentations, Each hybrid is a cascaded system, The first stage of both is a self-organizing feature map (SOFM), The second stages map distances into membership values, The third stage of one system is a multilayer perceptron (MLP), The third stage of the other is a bank of Choquet fuzzy integrals (FI's), The two systems are compared individually and as a combination to the baseline system, The new systems each perform better than the baseline system, The MLP system slightly outperforms the FI system; but the combination of the two outperforms the individual systems with a small increase in computational cost over the MLP system, Recognition rates of over 92% are achieved with a lexicon set having average size of 100, Experiments were performed on a standard test set from the SUNY/USPS CD-ROM database.