Abstract
The Human Visual System is a marvel of the world. People can readily recognise digits. But it is not as simple as it seems. The human brain has a million neurons and billions of connections between them, which makes this exceptionally complex task of image processing easier. People can effortlessly recognize digits. However, it turns into a challenging task for computers to recognize digits. Simple hunches about how to recognize digits become difficult to express algorithmically. Moreover, there is a significant variation in writing from person to person, which makes it immensely complex. The MNIST digit recognition system is the working of a machine to train itself so that it can recognize digits from different sources like emails, bank cheques, papers, images, etc. Convolutional Neural Networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, kernel size for CNN-based handwritten digit recognition. Our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy, along with reduced operational complexity and cost.
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