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Character recognition using LeNet-5

version 1.0.1 (370 KB) by Amir Ebrahimi
A deep model (LeNet-5) trained on the MNIST dataset is used for character recognition.

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Updated 06 May 2021

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The LeNet-5 model implemented in this project has 3 convolutional layers and 2 fully-connected layers. It has 62,000 training parameters, and the image input size is 32*32. This model achieved 98.48% accuracy on the MNIST test set after training on its train set. MNIST is a dataset of handwritten digits with 70,000 centred fixed-size grey-scale images. More details about the dataset are available in:

http://yann.lecun.com/exdb/mnist

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Cite As

Ebrahimi, Amir, et al. “Convolutional Neural Networks for Alzheimer’s Disease Detection on MRI Images.” Journal of Medical Imaging, vol. 8, no. 02, SPIE-Intl Soc Optical Eng, Apr. 2021, doi:10.1117/1.jmi.8.2.024503.

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MATLAB Release Compatibility
Created with R2020b
Compatible with R2019b and later releases
Platform Compatibility
Windows macOS Linux
Acknowledgements

Inspired by: Pre-trained 2D LeNet-5

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