This project is about enhancing Optical Character Recognition (OCR) using machine learning. The main script is website_recognition.ipynb which uses a trained model to predict digits from photos taken and drawings both from the website specially built for this project.
Once you retrieved this porject, execute the following command:
conda create --name <env> --file requirements.txtwhere <env> is the name you choose for this new environnement
You first need to locally run notebooks/number_recognition.ipynb which will train, validate and test a Sequential model with MNIST dataset.
This model will then be saved as a keras model in models/number_recon_model.keras
jupyterlab notebooks/number_recognition.ipynbThanks to Loke-60000's and fdeage's website including in this project, you can take photos of handwritted numbers. To do so :
cd website
python -m http.server 8001Your photos and drawings will be saved in your downloads' folder.
You need to copy them into this_project/data/webcam for the photos and this_project/data/drawings for the drawings.
Each file must be renamed with its own value (if several files have the same value, add underscores ("_") after the value). This will serve to display the actual value of the number and calculate the accuracy of the prediction.
Run website_recognition.ipynb to load your images, process them and use number_recon_model.keras to predict what number you draw.
jupyterlab website_recognition.ipynb