Source code for the AKHCRNet Paper: Deep neural architecture on bengali hand written character.
Proposal of a state of the art deep neural architectural solution for handwritten character recognition for Bengali alphabets, compound alphabets as well as numerical digits that achieves state-of-the-art accuracy 96.8% in just 11 epochs. Similar work has been done before by Chatterjee, Dutta, et al. 2019 but they achieved 96.12% accuracy in about 47 epochs. The deep neural architecture used in that paper was fairly large considering the inclusion of the weights of the ResNet 50 model which is a 50 layer Residual Network. This proposed model achieves higher accuracy as compared to any previous work & in a little number of epochs. ResNet50 is a good model trained on the ImageNet dataset, but I propose an HCR network that is trained from the scratch on Bengali characters without the "Ensemble Learning" that can outperform previous architectures.
Weight files can be downloaded from here. Or try the following code in a jupyter cell.
!wget https://github.com/theroyakash/AKHCRNet/releases/download/v1.0.0/model.h5