A hybrid neural network model in handwritten word recognition.

Abstract

A hybrid neural network model is developed and applied to handwritten word recognition. The word recognition system requires a module that assigns character class confidence values to segments of images of handwritten words. The module must accurately represent ambiguities between character classes and assign low confidence values to a wide variety of non-character segments resulting from erroneous segmentations. The proposed hybrid neural model is a cascaded system. The first stage is a self-organizing feature map algorithm (SOFM). The second stage maps distances into allograph membership values using a gradient descent learning algorithm. The third stage is a multi-layer feedforward network (MLFN). The new system performs better than the baseline system. Experiments were performed on a standard test set from the SUNY/USPS Database.