Handwritten Digit Recognition Based on the MNIST Dataset under PyTorch

Research Article
Open access

Handwritten Digit Recognition Based on the MNIST Dataset under PyTorch

Ruwei Wang 1*
  • 1 Shandong University of Finance and Economics    
  • *corresponding author wrw1227805945@163.com
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230216
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

Thanks to advancements in machine learning and artificial intelligence techniques, computers can now practice on data and learn from it in a manner that is similar to how the human brain works. Handwritten character and number identification has been one of the most pressing and fascinating subjects in pattern recognition and image processing. One of the most urgent and intriguing topics in pattern recognition and picture processing has been the identification of handwritten characters and numbers. As a crucial part of artificial intelligence, handwritten digit identification technology provides a vast array of application possibilities. The data demonstrates that, even though handwritten numbers are simply created with a few straightforward strokes, the appearance of numbers is more variable due to the various writing styles of each person. In this study, a deep learning framework-based upgraded LeNet-5 convolutional neural network model is used to build a handwritten number recognition model in Python. Automatic recognition of handwritten numbers will become the standard recognition technique if it can be applied to a wide range of industries, including banking and accounting, and hence save human costs.

Keywords:

MNIST, convolutional neural network, PyTorch, handwritten digit recognition, offline recognition, artificial intelligence

Wang,R. (2023). Handwritten Digit Recognition Based on the MNIST Dataset under PyTorch. Applied and Computational Engineering,8,432-437.
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References

[1]. Zhang, H. M. & Zhang, Kyauk J.. (2021). A review of research on offline handwritten digit recognition based on artificial intelligence. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition) (05), 83-91. doi:10.14132/j.cnki.1673-5439.2021.05.012.

[2]. Zong, Chunmei, Zhang, Yueqin & Shi, Ding. (2021).CNN-based handwritten digit recognition under PyTorch and application research. Computer and Digital Engineering (06), 1107-1112.

[3]. Tang, J.B., Li, W.J., Zhao, B. & Xi, L.P.. (2022). Research on handwritten digit recognition method based on convolutional neural network. Electronic Design Engineering (21), 189-193. doi:10.14022/j.issn1674-6236.2022.21.040.

[4]. Song Xiaoru,Wu Xue,Gao Song & Chen Chaobo. (2019). A study of handwritten digit recognition simulation based on deep neural networks. Science, Technology & Engineering (05), 193-196.

Cite this article

Wang,R. (2023). Handwritten Digit Recognition Based on the MNIST Dataset under PyTorch. Applied and Computational Engineering,8,432-437.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Zhang, H. M. & Zhang, Kyauk J.. (2021). A review of research on offline handwritten digit recognition based on artificial intelligence. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition) (05), 83-91. doi:10.14132/j.cnki.1673-5439.2021.05.012.

[2]. Zong, Chunmei, Zhang, Yueqin & Shi, Ding. (2021).CNN-based handwritten digit recognition under PyTorch and application research. Computer and Digital Engineering (06), 1107-1112.

[3]. Tang, J.B., Li, W.J., Zhao, B. & Xi, L.P.. (2022). Research on handwritten digit recognition method based on convolutional neural network. Electronic Design Engineering (21), 189-193. doi:10.14022/j.issn1674-6236.2022.21.040.

[4]. Song Xiaoru,Wu Xue,Gao Song & Chen Chaobo. (2019). A study of handwritten digit recognition simulation based on deep neural networks. Science, Technology & Engineering (05), 193-196.

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