Character recognition using LeNet-5

A deep model (LeNet-5) trained on the MNIST dataset is used for character recognition.


Updated 6 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:

Run the GUI and select your image.

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

Inspired by: Pre-trained 2D LeNet-5

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Version Published Release Notes

The relevant paper is published.