Abstract
The goal of this analysis has been on the development of handwritten digit recognition with the use of the MNIST dataset. In the latest days, the identification of handwritten digits has become a challenging research topic in machine learning. Due to physically formed digits having varying lengths, widths, orientations, and positions. It may be utilized in several ways, such as the amount and signature on bank checks, the location of postal and tax papers, and so on. This research used CNN for recognition. Total four steps followed by pre-processing, feature extraction, training CNN, classification, and recognition. Along with its great higher accuracy, CNN outperforms other methods in detecting essential characteristics without the need for human intervention. On top of that, it incorporates unique levels of convolution and pooling processes. Through CNN, 97.78% accuracy was obtained.
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Jayswal, D., Panchal, B.Y., Patel, B., Acharya, N., Nayak, R., Goel, P. (2023). Study and Develop a Convolutional Neural Network for MNIST Handwritten Digit Classification. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Rodrigues, J.J.P.C., Ganzha, M. (eds) Proceedings of Third International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-19-1142-2_32
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DOI: https://doi.org/10.1007/978-981-19-1142-2_32
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