Published: 30 July 2019| Version 1 | DOI: 10.17632/hf2tt9kxkn.1
Md Mahedi Hasan,


Automatic handwritten Bangla character recognition (HBCR) is a challenging problem in computer vision due to numerous variation in writing styles of an individual Bangla character and the presence of similarities with other characters in shapes. Considering the complexity of the problem, we need to develop modern convolutional neural network (CNN) for accurate recognition, but unfortunately at present, very few Bangla handwritten dataset contain a large number of image samples for each character suitable for training deep learning-based methods. In this paper, we present AIBangla, a new benchmark image database of isolated handwritten Bangla characters with detailed usage and baseline. Our dataset contains 80,403 hand-written images on 50 Bangla basic characters and 249,911 hand-written images on 171 Bangla unique pattern shapes compound characters written by more than 2,000 unique writers from various institutes across Bangladesh.



Manarat International University


Computer Vision, Optical Character Recognition, Bengali Language, Deep Learning