Abstract
Machine Learning is an important field of research in current trends. The extended field of machine learning is Deep Learning and is used for various research areas such as neural networks, image and signal processing, pattern recognition, etc. The handwritten digit recognition is an important task or process included in various applications such as car number plate recognition, staff identity number detection, etc. This paper proposed the design and analysis of various deep learning algorithms such as deep neural networks, convolutional neural networks, LeNet-5, AlexNet and MiniVGGNet for handwritten digit recognition using MNIST dataset.
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Acknowledgements
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the General Research Project under Grant number (R.G.P.2/241/43). I would like to thank King Khalid university for the necessary support to lead this paper, we thank our colleagues who sustained greatly assisted this research. We would also like to show our gratitude for sharing their pearls of wisdom with us during this research, and we thank “anonymous” reviewers for their so-called insights.
Funding
The Project was funded by King Khalid University, Grant No. R.G.P.2/241/43
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Alqahtani, A.S., Madheswari, A.N., Mubarakali, A. et al. Secure communication and implementation of handwritten digit recognition using deep neural network. Opt Quant Electron 55, 27 (2023). https://doi.org/10.1007/s11082-022-04290-7
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DOI: https://doi.org/10.1007/s11082-022-04290-7