DetectX: Real-Time Sign Language to Text Conversion

Language barriers remain a challenge for many individuals, especially for those who cannot speak or hear. DetectX aims to bridge this gap by enabling real-time translation of sign language into text, providing an accessible communication tool.

DetectX utilizes TensorFlow Object Detection and Python to create an end-to-end solution for real-time sign language detection.

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Features

• Real-time detection and translation of sign language into text.
• Built using TensorFlow, OpenCV, and Python.
• End-to-end pipeline from image collection to model deployment.

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How It Works

1. Image Collection:
   • Collect images of sign language gestures using a webcam and OpenCV.
2. Data Labeling:
   • Label the collected images with LabelImg to prepare the dataset.
3. Model Training:
   • Configure the TensorFlow Object Detection pipeline.
   • Use transfer learning to train the model for accurate detection.
4. Real-Time Detection:
   • Integrate the trained model with OpenCV for real-time sign language detection.

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Installation

1. Clone the repository:

   git clone https://github.com/your-username/DetectX.git

2. Navigate to the project directory:

      cd DetectX

3. Install dependencies:

    pip install -r requirements.txt

Usage

1. Collect Images: Run the script to capture images of gestures using your webcam:

     python collect_images.py

2. Label Data: Use the LabelImg tool to annotate your images for model training.
3. Train the Model: Train the TensorFlow Object Detection model with the labeled dataset:

    python train_model.py

4. Real-Time Detection: Run the real-time detection script:

   python detect_sign_language.py

Technologies Used

TensorFlow: For building and training the object detection model. OpenCV: For capturing images and performing real-time detection. Python: Backend programming and scripting. LabelImg: Data annotation tool for creating labeled datasets.

Future Enhancements

1. Add support for more sign language gestures.
2. Integrate with text-to-speech for enhanced accessibility.
3. Expand the model to support multiple sign languages.

Contributing

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.

Acknowledgments

Special thanks to the open-source community and resources that made this project possible. Inspired by the idea of breaking down communication barriers one step at a time.