  In spite of the advances in pattern recognition technology, Handwritten
Bangla Character Recognition (HBCR) (such as alpha-numeric and special
characters) remains largely unsolved due to the presence of many perplexing
characters and excessive cursive in Bangla handwriting. Even the best existing
recognizers do not lead to satisfactory performance for practical applications.
To improve the performance of Handwritten Bangla Digit Recognition (HBDR), we
herein present a new approach based on deep neural networks which have recently
shown excellent performance in many pattern recognition and machine learning
applications, but has not been throughly attempted for HBDR. We introduce
Bangla digit recognition techniques based on Deep Belief Network (DBN),
Convolutional Neural Networks (CNN), CNN with dropout, CNN with dropout and
Gaussian filters, and CNN with dropout and Gabor filters. These networks have
the advantage of extracting and using feature information, improving the
recognition of two dimensional shapes with a high degree of invariance to
translation, scaling and other pattern distortions. We systematically evaluated
the performance of our method on publicly available Bangla numeral image
database named CMATERdb 3.1.1. From experiments, we achieved 98.78% recognition
rate using the proposed method: CNN with Gabor features and dropout, which
outperforms the state-of-the-art algorithms for HDBR.
