Sign Language Learning Assistant

A machine learning-based application designed to help users learn and practice sign language through real-time detection and feedback.

🌟 Features

• Real-time sign language detection and recognition
• Interactive learning mode
• Practice mode with scoring system
• Model training capabilities
• Data collection tools
• Save and load trained models
• Performance testing mode
• Text-to-speech feedback
• GUI interface built with Tkinter

🚀 Getting Started

Prerequisites

• Python 3.7+
• Webcam or camera device

Required Dependencies

pip install -r requirements.txt

Dependencies list (requirements.txt):

opencv-python       # Computer vision and image processing
mediapipe          # Hand and pose detection framework
numpy              # Numerical computing and array operations
pickle             # Model serialization
tkinter            # GUI framework (usually comes with Python)
pyttsx3           # Text-to-speech conversion
Pillow            # Image processing library

Installation

1. Clone the repository:

git clone https://github.com/yourusername/sign-language-assistant.git
cd sign-language-assistant

2. Install required dependencies:

pip install -r requirements.txt

📱 Usage

The application offers several key functionalities through its intuitive interface:

Data Collection

• Click "Collect Data" to start gathering training data
• Follow the on-screen instructions to record signs
• Ensure proper lighting and camera positioning

Training

• Use "Train Model" to begin the training process
• Monitor the training progress in the status window
• Wait for completion notification

Detection

• Click "Start Detection" to begin real-time sign language recognition
• Position yourself in front of the camera
• Perform signs to see instant recognition results

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Testing

• Use "Start Test Mode" to evaluate your sign language skills
• Follow the prompts to perform specific signs
• Receive immediate feedback and scoring

Untitled.video.-.Made.with.Clipchamp.5.mp4

Model Management

• Save your trained models using "Save Model"
• Load previously trained models using "Load Model"
• Stop the current session using "Stop"

📊 Performance Metrics

• Real-time prediction display
• Accuracy scoring system
• Recent predictions history
• Status monitoring

🛠️ Technical Details

The application utilizes several key technologies:

• OpenCV (cv2): Handles video capture and image processing
• MediaPipe: Provides hand tracking and gesture recognition
• NumPy: Manages numerical operations and data processing
• Tkinter: Creates the graphical user interface
• pyttsx3: Enables text-to-speech feedback
• Threading: Ensures smooth GUI operation during processing
• Pickle: Handles model serialization and deserialization
• PIL (Pillow): Processes images for the GUI display

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

📮 Contact

For questions, feedback, or support, please open an issue in the GitHub repository.