.github/README.md

Optical Character Recognition

Use the tool live here.

This is an optical character recognition (OCR) tool that extracts characters from drawings. Using basic JS/HTML, we create a canvas to draw on, convert the drawing toa usable 28x28 pixel format, and send to the backend for prediction. In the backend, we use FastAPI to handle requests, with the only routes being \ for the home page and \predict for predictions. The prediction route uses a pre-trained CNN model that is able to recognize all English characters and digits. For more details on the architecture, the dataset, and training process, check out this writeup, which presents the simpler of two models trained for my writeup on CNNs (this one).

Usage

Once again, the tool is hosted here for easy access.

Draw the character you want to recognize on the left canvas. The right canvas will display the top-k predictions, where k can be adjusted using the slider below it. The slider is capped at 15 since, after 15, all of the predictions are basically at 0% probability.

To run this project locally, take the following steps:

Installation

1. Clone this repository.

git clone https://github.com/intelligent-username/OCR
cd OCR

2. Install the Python dependencies to a virtual environment.

python -m venv OCR-env
OCR-env\Scripts\activate  # On Windows
source OCR-env/bin/activate  # On macOS/Linux
pip install -r requirements.txt

3. Run the backend.

uvicorn backend.app:app --reload

4. Once the backend is running, go to http://127.0.0.1:8000/ in your web browser to access the frontend. This link will appear in the terminal when you run the backend.

License

This project is licensed under the MIT License. For details, see the LICENSE file.