Bengali Handwritten Character Recognition Using Deep Convolutional Neural Network

Handwritten character recognition is a nontrivial task as it seeks to recognize the correct class for user independent handwritten characters. This problem becomes even more challenging for a highly stylized, morphologically complex, and potentially juxtapositional characters comprising language like Bengali. As a result, the improvements over the years in Bengali character recognition are significantly less as compared to the other languages. In this paper, we propose a convolutional deep model to recognize Bengali handwritten characters. We first learnt a useful set of features by using kernels and local receptive fields, and then we have employed densely connected layers for the discrimination task. Our system has been tested on BanglaLekha-Isolated dataset. It achieves 98.66% accuracy on numerals (10 character classes), 94.99% accuracy on vowels (11 character classes), 91.60% accuracy on compound letters (20 character classes), 91.23% accuracy on alphabets (50 character classes), and 89.93% accuracy on almost all Bengali characters (80 character classes). Most of the errors incurred by our model in recognition task are due to extreme proximity in shapes among characters. A significant number of errors was caused by the mislabeled, irrecoverably distorted, and illegal data examples.