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2023 | OriginalPaper | Chapter

Study and Develop a Convolutional Neural Network for MNIST Handwritten Digit Classification

Authors : Disha Jayswal, Brijeshkumar Y. Panchal, Bansari Patel, Nidhi Acharya, Rikin Nayak, Parth Goel

Published in: Proceedings of Third International Conference on Computing, Communications, and Cyber-Security

Publisher: Springer Nature Singapore

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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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Metadata
Title
Study and Develop a Convolutional Neural Network for MNIST Handwritten Digit Classification
Authors
Disha Jayswal
Brijeshkumar Y. Panchal
Bansari Patel
Nidhi Acharya
Rikin Nayak
Parth Goel
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Proceedings of Third International Conference on Computing, Communications, and Cyber-Security

Print ISBN: 978-981-19-1141-5

Electronic ISBN: 978-981-19-1142-2

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-1142-2_32