SignSync: AI-Powered Sign Language Detector

SignSync is a real-time sign language detection system that translates hand gestures into text, bridging communication gaps for individuals with hearing or speech impairments. The project leverages computer vision, machine learning, and an interactive web interface for instant feedback and usability.

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🚀 Features

Real-Time Gesture Recognition

• Tracks hand gestures using MediaPipe’s 21 hand landmarks in live video.
• Processes frames in real-time for accurate gesture recognition.

Webcam-Based Input

• Captures input using a standard webcam; no additional hardware required.
• Continuously monitors hand positions and shapes for detection.

Machine Learning-Based Classification

• Uses RandomForestClassifier to classify hand gestures from landmarks.
• No deep learning (CNN) required—lightweight and fast.

Text-Only Output

• Converts recognized gestures into on-screen text.
• Provides clear communication without relying on audio.

Streamlit Web Interface

• Interactive frontend to view live webcam feed.
• Displays detected gestures in real-time with hand landmarks visualization.

Live Feedback via OpenCV

• Draws bounding boxes and landmarks on the video feed.
• Useful for user guidance and debugging.

Trainable and Extendable

• Easily add new gestures by collecting more data.
• Model can be retrained and saved using Pickle.

Accessibility-Focused

• Helps individuals with hearing or speech impairments communicate effectively.
• Bridges the gap between signers and non-signers.

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🛠️ Tech Stack

Frontend: Streamlit (interactive web interface)
Backend:

• OpenCV (video capture & processing)
• MediaPipe (hand tracking & landmark detection)
• NumPy (numerical operations)
• scikit-learn RandomForestClassifier (gesture recognition)
  Data Storage: Pickle (saving/loading trained models)
  Language: Python

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📊 Workflow

1. Data Collection – Capture gesture data using data_collection.py → saves dataset as CSV/NumPy arrays.
2. Data Processing – Clean and preprocess data with data_processing.py → generates normalized dataset.
3. Model Training – Train and save the model using model_training.py → produces model.pkl.
4. Real-Time Detection – Run sign_detection.py → detects gestures via webcam in real-time with live feedback.

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🎯 Future Improvements

Expand dataset to include full ASL/ISL alphabets & words

Incorporate Deep Learning (CNN/RNN) for higher accuracy

Add gesture-to-speech output

Deploy as a web or mobile application for broader accessibility

🤝 Acknowledgments

• MediaPipe by Google for hand landmark detection
• OpenCV for real-time computer vision
• Inspired by real-world AI applications for accessibility