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.
The world has been evolving with new technologies and advances everyday. With learning technologies, the research community can provide solutions in every aspect of life. However, it is found to lag behind the ability to explain its prediction. The current situation is such that these modern technologies can predict and decide upon various cases more accurately and speedily than a human, but has failed to provide an answer when the question of “how” it arrived at such a prediction or “why” one must trust its prediction, is put forward. To attain a deeper understanding of this rising trend, the authors surveyed a very recent and talked-about novel contribution, “explainability,” which would provide rich insight on a prediction being made by a model. The central premise of this chapter is to provide an overview of studies explored in the domain and obtain an idea of the current scenario along with the advancements achieved to date in this field. This survey aims to provide a comprehensive background of the broad spectrum of “explainability.”
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