AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning

AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning

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.

AKHCRNet：使用深度学习的孟加拉语手写字符识别

我提出了一种先进的深度神经体系结构解决方案，用于孟加拉语字母，复合字符以及数字的手写字符识别，该解决方案在短短11个时期内可达到96.8％的最新精度。Chatterjee，Swagato等人之前已经做过类似的工作。但他们在大约47个纪元内达到了96.12％的准确性。.. 考虑到包括50层残差网络的ResNet 50模型的权重，该论文中使用的深度神经体系结构相当大。与任何以前的工作相比，该建议的模型在少数几个时期内都实现了更高的准确性。ResNet50是在ImageNet数据集上训练的一个很好的模型，但是我提出了一个HCR网络，该网络从头开始对孟加拉语字符进行了训练，而没有能胜过以前体系结构的“集成学习”。 （阅读更多）