Configurable hybrid architectures for character recognition applications

Character recognition is a multiclass problem with typically large number of classes. Hierarchical classifiers are found to be suitable for such classification problems due to their low space and time complexities. However, most hierarchical classifiers employ similar classifiers at different levels of the hierarchy. This may not be ideal, as the complexity of classification is not same at all the stages. In this paper, we propose an algorithm to build a hybrid hierarchical classifier by choosing the classifiers of appropriate complexity at each level of the hierarchy. We demonstrate that hierarchical combination of complex classifiers improves the classification performance significantly.