  In this paper, we propose a solution which uses state-of-the-art techniques
in Deep Learning to tackle the problem of Bengali Handwritten Character
Recognition ( HCR ). Our method uses lesser iterations to train than most other
comparable methods. We employ Transfer Learning on ResNet 50, a
state-of-the-art deep Convolutional Neural Network Model, pretrained on
ImageNet dataset. We also use other techniques like a modified version of One
Cycle Policy, varying the input image sizes etc. to ensure that our training
occurs fast. We use the BanglaLekha-Isolated Dataset for evaluation of our
technique which consists of 84 classes (50 Basic, 10 Numerals and 24 Compound
Characters). We are able to achieve 96.12% accuracy in just 47 epochs on
BanglaLekha-Isolated dataset. When comparing our method with that of other
researchers, considering number of classes and without using Ensemble Learning,
the proposed solution achieves state of the art result for Handwritten Bengali
Character Recognition. Code and weight files are available at
https://github.com/swagato-c/bangla-hwcr-present.
