A simple hand-written digit classifier which can classify digits with 98% accuracy.
You can visit the website to try this yourself.
- We are using the MNIST dataset to train and test our model to recognise digits.
- You can view the dataset here.
- A dense neural network with architecture:
| Layer | Activation | Neurons |
|---|---|---|
| 1 | ReLU | 784 |
| 2 | ReLU | 10 |
| 3 | Softmax | 10 |
- Trained on the MNIST dataset for 60 epochs using back propagation.
- A website made using Vue js to test and showcase the classification prowess of the model.
- You can visit the website.
cdto where you want the project to live.- Create a virtual environment and activate it.
optional
- Create a virtual environment and activate it.
- To set up the project, run these commands:
git clone "git@github.com:Varun487/WrittenDigitClassifier.git"
cd WrittenDigitClassifier
pip install -r requirements.txt
- To run the jupyter notebook:
jupter notebook
- To build the UI:
npm run build
This project is inspired by a medium article by Carlos Aguayo.