Sign-Sense: Amharic Sign Language Recognition

Sign-Sense is an innovative project aimed at recognizing Amharic sign language gestures using computer vision and machine learning techniques. This project leverages OpenCV for image processing, FreeType for rendering Amharic text, and a pre-trained model for accurate gesture classification. It seeks to bridge the communication gap by enabling seamless interaction through Amharic sign language.

Project Overview

Sign-Sense provides a solution for recognizing Amharic sign language gestures with high accuracy, offering:

• Gesture classification using a pre-trained machine learning model.
• Real-time hand gesture data collection.
• Rendering of Amharic text on images to display recognized gestures.

Project Structure

The repository contains the following files:

Scripts

• dataCollection.py: Script to collect hand gesture data using a webcam.
• fep.py: Script to render Amharic text on an image using FreeType and OpenCV.
• test.py: Script to test hand gesture recognition using a pre-trained model.

Configuration & Dependencies

• .gitignore: Specifies files and directories to be ignored by Git.
• settings.json: VS Code settings for the project configuration.
• labels.txt: A list of labels representing hand gestures.
• requirements.txt: List of dependencies required to run the project.

Setup

Follow these steps to set up the project on your local machine:

1. Clone the repository:

   git clone https://github.com/yourusername/Sign-Sense.git
   cd Sign-Sense

2. Install the required dependencies:

   pip install -r requirements.txt

3. Ensure you have a webcam connected to your system for gesture data collection.

Usage

Data Collection

To collect hand gesture data, run the dataCollection.py script:

python dataCollection.py

• Press s to save an image of the current hand gesture.
• Press q to quit the data collection process.

Amharic Text Rendering

To render Amharic text on an image, run the fep.py script:

python fep.py

This script will overlay Amharic text on images using FreeType and OpenCV.

Gesture Recognition

To test the hand gesture recognition, run the test.py script:

python test.py

• The system will classify gestures based on a pre-trained model.
• Press q to quit the recognition process.

License

This project is licensed under the MIT License.

Acknowledgements

• OpenCV – For image processing and computer vision capabilities.
• FreeType – For rendering Amharic text in the application.
• cvzone – For additional computer vision tools.

Feel free to contribute to this project by submitting issues or pull requests. Your contributions are welcome!