Recently, a few works on recognition of handwritten Bangla characters have been reported in the literature. However, there is scope for further research in this area. In the present article, results of our recent study on recognition of handwritten Bangla basic characters will be reported. This is a 50 class problem since the alphabet of Bangla has 50 basic characters. In this study, features are obtained by computing local chain code histograms of input character shape. Comparative recognition results are obtained between computation of the above feature based on the contour and one-pixel skeletal representations of the input character image. Also, the classification results are obtained after down sampling the histogram feature by applying Gaussian filter in both these cases. Multilayer perceptrons (MLP) trained by back propagation (BP) algorithm are used as classifiers in the present study. Near exhaustive studies are done for selection of its hidden layer size. An analysis of the misclassified samples shows an interesting error pattern and this has been used for further improvement in the recognition results. Final recognition accuracies on the training and the test sets are respectively 94.65% and 92.14%.
['Ujjwal Bhattacharya', 'Malayappan Shridhar', 'Swapan K. Parui']
On Recognition of handwritten bangla characters