DigiNet: Prediction of Assamese handwritten digits using convolutional neural network

Abstract

Numerous work focusing on Indian Languages for automatically recognizing characters has been witnessed in literature in the last few decades. But it was observed that only a handful of them targeted the optical character recognition of the Assamese language despite the language being widely spoken and used in many official works and activities across North-East India. The limited contribution in this field seems to be inadequate and insufficient in their ability to recognize the handwritten characters due to dissimilarity in the people's writing style. This has motivated us to device a computational model for automatic recognition of the handwritten Assamese digits belonging to 10 different classes of 0 to 9. This study employs a convolutional neural network model (DigiNet) to learn and understand the various styles of handwritten digits. Our model exercises the convolutional neural network composed of six alternative Convolution and Pooling layers and is able to attain state-of-the-art performance on the Assamese handwritten digits, achieving a test accuracy of 93.02% which is a pretty good success. The proficiency of the model architecture is also tested on the MNIST and the Bangla handwritten numeral datasets. Furthermore, our proposed model is compared with the pre-trained VGG 19 architecture, where the performance of our model was better compared to the pre-trained model on the Assamese as well as the Bangla handwritten numerals.

Handwritten Gujarati Numerals Classification Based on Deep Convolution Neural Networks Using Transfer Learning Scenarios

“…They obtained 82.15% test accuracy [37]. In [38], the authors employed a convolutional neural network to classify the handwritten Assamese digits and also compared it with VGG16 pre-trained networks. They have reported 93.02% testing accuracy.…”