A Majority Voting Scheme for Multiresolution Recognition of Handprinted Numerals

This paper proposes a simple voting scheme for off-line recognition of handprinted numerals. One of the main features of the proposed scheme is that this is not script dependent. Another interesting feature is that it is sufficiently fast for real-life applications. In contrast to the usual practices, here we studied the efficiency of a majority voting approach when all the classifiers involved are multilayer perceptrons (MLP) of different sizes and respective features are based on wavelet transforms at different resolution levels. The rationale for this approach is to explore how one can improve the recognition performance without adding much to the requirements for computational time and resources. For simplicity and efficiency, in the present work, we considered only three coarse-to-fine resolution levels of wavelet representation. We primarily simulated the proposed technique on a database of off-line handprinted Bangla (a major Indian script) numerals. We achieved 97.16% correct recognition rate on a test set of 5000 Bangla numerals. In this simulation we used two other disjoint sets (one for training and the other for validation purpose) of sizes 6000 and 1000 respectively. We have also tested our approach on MNIST database for handwritten English digits. The result is comparable with state-of-the-art technologies.