A Comparison of Feature and Pixel-based Methods for Recognizing Handwritten Bangla Digits

We propose a novel handwritten character recognition method for isolated handwritten Bangla digits. A feature is introduced for such patterns, the contour angular technique. It is compared to other methods, such as the hotspot feature, the gray-level normalized character image and a basic low-resolution pixel-based method. One of the goals of this study is to explore performance differences between dedicated feature methods and the pixel-based methods. The four methods are compared with support vector machine (SVM) classifiers on the collection of handwritten Bangla digit images. The results show that the fast contour angular technique outperforms the other techniques when not very many training examples are used. The fast contour angular technique captures aspects of curvature of the handwritten image and results in much faster character classification than the gray pixel-based method. Still, this feature obtains a similar recognition compared to the gray pixel-based method when a large training set is used. In order to investigate further whether the different feature methods represent complementary aspects of shape, the effect of majority voting is explored. The results indicate that the majority voting method achieves the best recognition performance on this dataset.