Sign Language Detection Using YOLOv5

Detect Sign Language with YOLOv5

Welcome to the Sign Language Detection project! This application utilizes YOLOv5 to detect and classify sign language gestures in real-time. The project is designed to aid in communication for the deaf and hard-of-hearing community by providing a tool to translate sign language into text.

What This Project Does

This project leverages the power of the YOLOv5 object detection model to accurately detect and classify various sign language gestures. By using a webcam or video input, the model can identify specific signs and translate them into text, making communication more accessible.

Demonstration (Picture)

Installation and Usage Instructions

If you would like to run the application locally or contribute to its development, follow these steps:

Prerequisites

Ensure you have Python 3.x installed on your machine. You can download it from python.org.

Model Training

1. Clone the YOLOv5 repository:

   !git clone https://github.com/ultralytics/yolov5.git

2. Navigate to the yolov5 directory and install the necessary dependencies:

   %cd yolov5
   !pip install -qr requirements.txt

3. Open sign-lang-detection.ipynb in Jupyter Notebook or Google Colab.
4. Follow the instructions to configure and train the YOLOv5 model using the collected data.
5. The trained model weights will be saved in the runs/train/yolov5s_results/weights directory.

Model Inference

1. Enter the yolov5 directory.
2. Run the run.py file using your preferred code editor or via the terminal:

   python run.py

File Descriptions

• data-collection.ipynb: Notebook for collecting and labeling sign language images.
• sign-lang-detection.ipynb: Notebook for training the YOLOv5 model on the collected sign language data.
• yolov5: Directory containing the YOLOv5 model and related scripts.

Contributor Expectations

If you would like to contribute to the Sign Language Detection project, please follow these guidelines:

1. Fork the repository and create your feature branch (git checkout -b feature/AmazingFeature).
2. Commit your changes (git commit -m 'Add some AmazingFeature').
3. Push to the branch (git push origin feature/AmazingFeature).
4. Open a pull request.

Before contributing, ensure your code adheres to the existing style and includes tests where appropriate. Contributions that improve the application's functionality, performance, or usability are highly appreciated.

Credits

This project was created following the excellent tutorials in the Sign Language Detection YouTube Playlist. Special thanks to the content creator for providing comprehensive guidance on building this project.

Thank you for using the Sign Language Detection project!