Abstract
This paper discusses the various fundamental techniques and algorithms which are used in neural networks to classify the handwritten digits for the smarter applications in this twenty-first century. This paper gives the deep learning of different tools which is used to classify handwritten digits. The maximum accuracy we achieve from this kind of techniques is approximately 96%. But this accuracy can be improved by advanced techniques to 99%. In recent days, the applications of neural network have been increased tremendously in various fields such as bank and post office to classify handwritten patterns.
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Ambikapathy, Singh, A.V. (2018). Fundamental Concepts of Neural Networks and Deep Learning of Different Techniques to Classify the Handwritten Digits. In: Konkani, A., Bera, R., Paul, S. (eds) Advances in Systems, Control and Automation. Lecture Notes in Electrical Engineering, vol 442. Springer, Singapore. https://doi.org/10.1007/978-981-10-4762-6_27
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DOI: https://doi.org/10.1007/978-981-10-4762-6_27
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