Sign Language Recognition

A real-time sign language recognition system leveraging deep learning and computer vision. It interprets hand gestures with high accuracy and delivers quick responses for a smooth user experience.

Key Features

• High Accuracy – Over 90% accuracy using a custom-trained CNN model
• Fast Response – Flask-based REST API with response times under 200ms
• Interactive Frontend – React-based UI for real-time gesture recognition

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Prerequisites

• Python 3.11
• Node.js & npm
• A webcam connected to your machine

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Getting Started

1. Clone the Repository

git clone https://github.com/ankur1409/Sign-Language.git
cd Sign-Language

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2. Backend Setup

cd backend

# Create a virtual environment using Python 3.11
py -3.11 -m venv venv

# Activate the virtual environment
# On Git Bash / macOS / Linux:
source ./venv/Scripts/activate
# On Windows (Command Prompt / PowerShell):
.\venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Run the Backend Server

# Make sure the venv is activated, then:
python server.py

# OR without activating:
./venv/Scripts/python server.py

The Flask server will start at: http://127.0.0.1:8080

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3. Frontend Setup

cd frontend

# Install dependencies
npm install

# Start the development server
npm run dev

The React app will start at: http://localhost:5173

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Running Both Together

Open two separate terminals:

Terminal	Command
Terminal 1 (Backend)	cd backend && ./venv/Scripts/python server.py
Terminal 2 (Frontend)	cd frontend && npm run dev

Then open your browser at http://localhost:5173 and allow webcam access.

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API Endpoints

Method	Endpoint	Description
GET	/video_feed	Live MJPEG video stream with predictions
GET	/get_prediction	Returns the latest predicted sign as JSON

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Project Structure

Sign-Language/
├── backend/
│   ├── server.py          # Flask API server
│   ├── predictions.py     # Prediction logic using the trained model
│   ├── model_CNN.py       # CNN model definition
│   ├── collect.py         # Data collection script
│   ├── model_json         # Saved model architecture (JSON)
│   ├── model_json.weights.h5  # Trained model weights
│   └── requirements.txt   # Python dependencies
└── frontend/
    ├── src/               # React source files
    └── package.json       # Node dependencies