Bangla Handwritten Character and Digit Recognition Using Deep Convolutional Neural Network on Augmented Dataset and Its Applications
Hasibul Huda, Md. Ariful Islam Fahad, Moonmoon Islam, Amit Kumar Das
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.