  I propose a state of the art deep neural architectural solution for
handwritten character recognition for Bengali alphabets, compound characters 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, Swagato, et al. 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.
