On the choice of training set, architecture and combination rule of multiple MLP classifiers for multiresolution recognition of handwritten characters

A script independent recognition scheme for handwritten characters using multiple MLP classifiers and wavelet transform-based multiresolution pixel features is presented. We studied four different approaches for combination of multiple MLP classifiers and observed that a weighted majority voting approach provided the best recognition performance. Also, a thumb rule for the selection of network architecture has been obtained and a dynamic strategy for selection of training samples has been studied. The dynamic training set selection approach often makes the training procedure several times faster than the traditional training scheme. In our simulations, 98.04% recognition accuracy has been obtained on a test set of 5000 handwritten Bangla (an Indian script) numerals. Our approach is sufficiently fast for its real life applications and also script independent. The recognition performance of the present approach on the MNIST database for handwritten English digits is comparable to the state-of-the-art technologies.