Detection of ambiguous patterns in a SOM based recognition system: application to handwritten numeral classification

This work presents a system for pattern recognition that combines a self-organising unsupervised technique (via a Kohonen-type SOM) with a bayesian strategy in order to classify input patterns from a given probability distribution and, at the same time, detect ambiguous cases and explain answers. We apply the system to the recognition of handwritten digits. This proposal is intended as an improvement of a model previously introduced by our group, consisting basically of a hybrid unsupervised, selforganising model, followed by a supervised stage. Experiments were carried out on the handwritten digit database of Concordia University, which is generally accepted as one of the standards in most of the literature in the field.