DeepBanglaNet: A Deep Convolutional Neural Network to Recognize Bengali Handwritten Digits
Tanvir Mahmud, Abdul Rakib Hossain, Shaikh Anowarul Fattah · 2020 IEEE Region 10 Symposium (TENSYMP) · 2020
Classifying handwritten digits is one of the most trending topics of research in the study of the automated text recognition system. The problem is more challenging in the case of Bengali digits due to additional complexities arising from similarity among various digits along with a wide variety of styles of hand-writings. In this paper, an end-to-end deep convolutional neural network, named as DeepBanglaNet, is proposed to classify Bengali handwritten digits. The proposed network utilizes various state-of-the-art optimization algorithms for eliminating vanishing/exploding gradient problems while extracting the global features effectively required for proper recognition of handwritten digits. This results in a very efficient model providing state-of-the-art accuracy of 99.43% on the NumtaDB database and outperforms all other existing models in all traditional evaluation metrics.