Trainable Multiple Classifier Schemes for Handwritten Character Recognition

In this paper we propose two novel multiple classifier fusion schemes which, although different in terms of architecture, share the idea of dynamically extracting additional statistical information about the individually trained participant classifiers by reinterpreting their outputs on a validation set. This is achieved through training on the resulting intermediate feature spaces of another classifier, be it a combiner or an intermediate stage classification device. We subsequently implemented our proposals as multi-classifier systems for handwritten character recognition and compare the performance obtained through a series of cross-validation experiments of increasing difficulty. Our findings strongly suggest that both schemes can successfully overcome the limitations imposed on fixed combination strategies from the requirement of comparable performance levels among their participant classifiers. In addition, the results presented demonstrate the significant gains achieved by our proposals in comparison with both individual classifiers experimentally optimized for the task in hand, and a multi-classifier system design process which incorporates artificial intelligence techniques.