Bangla Handwritten Digit Recognition Using Deep CNN for Large and Unbiased Dataset

Abstract

Bangla handwritten digit recognition is a convenient starting point for building an OCR in the Bengali language. Lack of large and unbiased dataset, Bangla digit recognition was not standardized previously. But in this paper, a large and unbiased dataset known as NumtaDB is used for Bangla digit recognition. The challenges of the NumtaDB dataset are highly unprocessed and augmented images. So different kinds of preprocessing techniques are used for processing images and deep convolutional neural network (CNN) is used as the classification model in this paper. The deep convolutional neural network model has shown an excellent performance, securing the 13th position with 92.72% testing accuracy in the Bengali handwritten digit recognition challenge 2018 among 57 participating teams. A study of the network performance on the MNIST and EMNIST datasets were performed in order to bolster the analysis.