AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning
This is an incremental improvement for Bengali language processing, offering faster training with slightly higher accuracy.
The paper tackles Bengali handwritten character recognition by proposing a deep neural network that achieves 96.8% accuracy in 11 epochs, outperforming prior work that reached 96.12% accuracy in 47 epochs.
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.