🧠 Sign-Language-Detection-Model

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📝 Overview

This project is a Real-Time Sign Language Detection System that recognizes English alphabet signs (A–Z) using a webcam.
It leverages MediaPipe for hand landmark detection and a Machine Learning model trained with custom-collected gesture data.

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🚀 Features

✨ Real-time gesture recognition using OpenCV
✋ Hand landmark tracking via MediaPipe Hands
🧠 Custom-trained ML model for A–Z alphabet detection
⚡ Lightweight and efficient — runs smoothly on most systems
💾 Includes data collection, model training, and prediction scripts

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🛠️ Tech Stack

Category	Technologies
Language	Python
Libraries	OpenCV, MediaPipe, NumPy, Scikit-learn, Pickle
Model	Random Forest Regressor
Environment	Jupyter / VS Code / PyCharm

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Project Structure

📁 Sign-Language-Detection-Model/
├── collect_images.py      # Capture and save hand gesture data  
├── create_dataset.py      # Organize and preprocess collected data  
├── train_classifier.py    # Train the Random Forest Regressor model  
├── test_model.p           # Run real-time sign recognition  
└── requirements.txt       # Dependencies and versions  

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🧩 How It Works

1. 🖼️ Data Collection – Capture gesture images for each alphabet using collect_images.py.
2. 🧮 Dataset Creation – Extract and save hand landmarks with create_dataset.py.
3. 🧠 Model Training – Train the Random Forest classifier using train_classifier.py.
4. 🔍 Real-Time Detection – Use your webcam to detect and predict signs live.

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🎯 Future Enhancements

🚀 Support for dynamic gestures (words or phrases) — with a larger dataset and GPU training
🧬 Integration with CNN / Deep Learning models for better accuracy
🔊 Add audio feedback for recognized signs
🪄 Build a user-friendly GUI for interaction