A connectionist expert system model for conflict resolution in unconstrained handwritten numeral recognition

The paper describes a neural network and expert system model for conflict resolution of unconstrained handwritten numerals. 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 identified by the neural network. The neural network classifier is a combination of Modified Self-Organizing Map (MSOM) and Learning Vector Quantization (LVQ). The experiments are conducted on Self-Organizing Map (SOM), MSOM, and the combinations of SOM & LVQ and MSOM & LVQ techniques. These experiments on a database of unconstrained handwritten samples show that the combination of MSOM & LVQ achieves satisfactory results in terms of classification, recognition and training time. 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 developed system is found to be useful in resolving conflicts in the recognition of Unconstrained Handwritten Numerals of PIN or ZIP codes of mailing addresses.