🤖 Sign Language Detection Using Python

Hand Gesture Recognition System for Accessibility

A final-year level Computer Vision + Machine Learning project that detects hand gestures in real-time and interprets them as sign language, enabling better communication support for hearing and speech impaired individuals.

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📌 Project Overview

Sign language is one of the primary communication methods for the deaf and mute community.
This project focuses on building an intelligent gesture recognition system that can:

• Capture hand movements using a webcam
• Detect and classify gestures
• Convert gestures into meaningful sign language output
  This system serves as an assistive technology solution for accessibility.

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🎯 Objectives

• ✅ Detect human hand gestures using computer vision
• ✅ Recognize sign language gestures accurately
• ✅ Provide real-time prediction output
• ✅ Enable accessibility-focused communication support
• ✅ Build a scalable model for future gesture expansion

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🚀 Key Features

• Real-time hand tracking using webcam
• Gesture recognition using Machine Learning
• Sign-to-text translation support
• High accuracy with optimized preprocessing
• Extendable for Indian Sign Language (ISL) dataset

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🛠️ Technologies Used

Component	Tools/Frameworks
Programming Language	Python
Computer Vision	OpenCV
Hand Landmark Detection	MediaPipe
Machine Learning	Scikit-learn / TensorFlow (if used)
Numerical Processing	NumPy
Dataset Handling	Custom Gesture Dataset

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📂 Project Structure

Sign-Language-Detection-Using-Python/
│
├── dataset/                  # Gesture dataset files (if included)
├── models/                   # Saved ML models
├── src/                      # Source code files
│   ├── main.py               # Main execution file
│   ├── gesture_recognition.py
│   └── utils.py
│
├── requirements.txt          # Dependencies
├── README.md                 # Documentation
└── demo/                     # Output screenshots or videos


⚙️ How It Works (Methodology)
1. Video Capture
Webcam captures hand movements frame-by-frame
2. Hand Landmark Detection
MediaPipe identifies finger joint coordinates
3. Feature Extraction
Landmark points are converted into feature vectors
4. Gesture Classification
ML model predicts gesture category
5. Output Interpretation

Gesture is mapped into sign/text output
✅ Installation & Setup
Step 1: Clone Repository
git clone https://github.com/bot041/Sign-Language-Detection-Using-Python.git
cd Sign-Language-Detection-Using-Python
Step 2: Install Dependencies
pip install -r requirements.txt
Step 3: Run the Application
python main.py

📊 Results
Achieved real-time gesture recognition performance
Accurate detection under normal lighting conditions
Extendable for more gesture classes and ISL support

🔮 Future Enhancements
✅ Full Sign-to-Speech conversion
✅ Support for Indian Sign Language (ISL) vocabulary
✅ Deep Learning CNN/LSTM based gesture recognition
✅ Mobile App Deployment
✅ Two-way translation (speech ↔ sign)

👨‍🎓 Academic Use
This project is suitable for:
Final Year Engineering Project
Assistive AI Systems
Computer Vision Research
Human-Computer Interaction

👤 Author
Developed by bot041
GitHub Profile: https://github.com/bot041
Email: bkambad041@gmail.com