P9: Decoding Sign Language Using AI, OpenCV, and MediaPipe

This project leverages the power of AI and computer vision to decode sign language gestures into actionable outputs, bridging communication gaps for people who rely on sign language. By utilizing Python 3, OpenCV, and MediaPipe, the system tracks and interprets hand gestures in real-time with high precision. Additionally, it integrates a custom-trained machine learning model to enhance gesture recognition accuracy and adaptability.

🔧 Tools and Libraries Used

• Python 3: The backbone programming language for developing and integrating the project, known for its versatility and extensive libraries.

• OpenCV: A robust library for real-time computer vision that enables video capture, image processing, and analysis of hand movements.

• MediaPipe: A pre-trained machine learning framework that facilitates hand and pose tracking, providing accurate hand landmark detection and gesture identification.

📋 Project Workflow

1. Install and Import Dependencies: Set up the Python environment by installing libraries like OpenCV, MediaPipe, and Scikit-Learn. Import all required modules to enable smooth execution.

2. Make Some Detections: Use MediaPipe’s hand-tracking solution to identify and map key hand landmarks in a video stream. This provides the foundation for detecting specific gestures.

3. Capture Landmarks & Export to CSV: Extract the positional data of hand landmarks and save it to a CSV file. This dataset serves as the input for training the machine learning model.

4. Train Custom Model Using Scikit Learn: Build a gesture recognition model using Scikit-Learn. Train the model with the captured landmark data to classify hand gestures effectively.

5. Make Detections with Model: Integrate the trained model into the live system. The model processes real-time input and maps gestures to corresponding actions or messages.

✨ Let’s Collaborate!

I’m excited to share this project with the tech community and learn from your insights. If you have ideas to enhance this sign language decoder—such as integrating more gestures, improving model accuracy, or adding multilingual support—let’s connect! Share your suggestions, feedback, or creative applications in the comments below. I’d love to explore innovative possibilities and collaborate with you. 😊

🔗 Explore the project here: https://github.com/iamramzan/P9-Decoding-Sign-Language-Using-AI-OpenCV-and-MediaPipe

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