AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning

29 Aug 2020 Akash Roy

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... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Handwriting Recognition BanglaLekha Isolated Dataset AKHCRNet Accuracy 96.8 # 1
Epochs 11 # 1
Cross Entropy Loss 0.21612 # 1
Transfer Learning BanglaLekha Isolated Dataset Chatterjee, Dutta et al.[1] Transfer Learning on ResNet-50 91.13 % (50 Char) + 98.42% (Numbers) Accuracy 96.12 # 1

Methods used in the Paper