Neural network based expert system model for conflict resolution

The paper describes a neural network and expert system model for conflict resolution of unconstrained handwritten characters and it completely resolves the confusion between the conflicting characters. The basic recognizer is the neural network. The neural network classifier is a combination of Modified Self-Organizing Map (MSOM) and Learning Vector Quantization (LVQ). It will solve most of the cases, but will fall in certain confusing cases. The expert system, the second recognizer, resolves the confusions generated by the neural network. The results obtained from this two-tier architecture are compared with the comments collected from an experiment conducted with a group of human experts specialized in unconstrained handwritten character recognition. The substitution error is eliminated.