Bangla Handwritten Character and Digit Recognition Using Deep Convolutional Neural Network on Augmented Dataset and Its Applications

Abstract

Bangla Handwritten digit and character recognition, a complex computer vision problem that is important for the Bengali language as the progress in this segment for the Bengali language is slow. We used two popular datasets, BanglaLekha-Isolated and NumbtaDB, for both digits and characters and used a Convolutional neural network to train our model. We augmented our dataset using a shifting method and ran multiple experiments on vowels, digits, and characters. The result is 96.42% average accuracy on BanglaLekha augmented. Our model also achieved 98.92% accuracy on the NumtaDB dataset. We used our model to sketch up two models, License plate recognition and Smart E-learning application. We used connected component analysis in License plate recognition that helped us to extract essential segments of the license plate. We used Keras as a TensorFlow backend in our research. Bangla OCR research is ongoing and will get better over time with better datasets and learning techniques.