Sign Language Detection using Computer Vision

A real-time sign language detection system built using Computer Vision and Machine Learning techniques.
The project captures hand gestures through a webcam, extracts landmark features, and predicts sign language gestures with high accuracy.

Designed to explore the intersection of AI, accessibility, and human-computer interaction.

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Overview

This project focuses on detecting and classifying sign language gestures in real time using hand tracking and deep learning techniques.

The system:

• Captures live video input
• Detects hand landmarks
• Extracts gesture features
• Runs prediction through a trained model
• Displays detected sign instantly

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Core Features

• Real-time gesture recognition
• Hand landmark detection
• Live webcam inference
• Machine learning based classification
• Efficient preprocessing pipeline
• Scalable architecture for custom gestures
• Low latency predictions

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Tech Stack

Technology	Usage
Python	Core development
OpenCV	Video processing
MediaPipe	Hand tracking & landmarks
TensorFlow / Keras	Model training
NumPy	Numerical computation
Scikit-learn	ML utilities
Matplotlib	Visualization

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Project Structure

sign-language-detection/
│
├── dataset/            # Gesture dataset
├── model/              # Saved trained models
├── notebooks/          # Experiments & training notebooks
├── src/                # Source files
├── assets/             # Screenshots / demo gifs
├── requirements.txt
├── main.py
└── README.md

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Installation

Clone the repository:

git clone https://github.com/your-username/sign-language-detection.git

Move into the project directory:

cd sign-language-detection

Install dependencies:

pip install -r requirements.txt

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Running the Project

python main.py

The webcam feed will open and begin detecting sign language gestures in real time.

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Working Pipeline

Webcam Feed
     ↓
Hand Detection
     ↓
Landmark Extraction
     ↓
Feature Processing
     ↓
ML Model Prediction
     ↓
Detected Gesture Output

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Model Capabilities

• Real-time hand tracking
• Gesture classification
• Landmark-based feature extraction
• Fast inference performance
• Multi-gesture scalability

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Future Enhancements

• Sentence generation from gestures
• Speech synthesis integration
• Transformer-based gesture recognition
• Web deployment
• Mobile support
• Real-time translation pipeline

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Motivation

The goal of this project is to explore how AI-powered vision systems can improve accessibility and create more natural communication interfaces.

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Author

Ayush Kar

Computer Science Engineer focused on:

• Artificial Intelligence
• Computer Vision
• Full Stack Development
• Intelligent Interactive Systems

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License

This project is licensed under the MIT License.

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Repository Support

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