A neuro-expert system model for conflict resolution

We describe here a neural network and expert system model for conflict resolution of unconstrained handwritten numerals. The neural network classifier is a combination of modified self-organizing map and learning vector quantization. The basic recognizer is the neural network. It solves most of the cases, but fails 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 those of a combination of four algorithms. This work shows that it is possible to eliminate the substitution while maintaining a fairly high recognition.