AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning

EasyChair Preprint 3965

Abstract

I propose 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.

Keyphrases: Convolutional Networks, Handwritten Character Recognition, deep learning, image recognition

@booklet{EasyChair:3965,
  author    = {Akash Roy},
  title     = {AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning},
  howpublished = {EasyChair Preprint 3965},
  year      = {EasyChair, 2020}}