Fusion of combination rules of an ensemble of MLP classifiers for improved recognition accuracy of handprinted Bangia numerals

In handwritten character recognition problem, the input images are often affected by distortions and noise. Thus such images at different resolutions include different variations in the input data. In the present work, we considered wavelet transform to obtain multi-resolution representation of each input character image. At each resolution level, we considered three MLPs with different numbers of nodes in their hidden layers and combined the outputs produced by all the MLPs of the whole ensemble by using weighted sum rule, product rule and majority voting. The set of misclassified samples produced by one combination rule is neither a subset nor a superset of a similar set produced by another rule. So, majority voting has been used for the second and final round to produce final outputs after combining the results of the three combinations of the first stage. The proposed approach produced 99.10% correct recognition rate on the test set of Bangia (a major Indian script) numeral database.