Sign Language Gesture Recognition System 🤟

A real-time sign language recognition system that uses MediaPipe hand landmark detection and a trained Keras deep learning model to recognize hand gestures via webcam.

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📌 Project Overview

This system detects hand landmarks in real-time using your webcam and classifies them into one of 6 predefined sign language gestures. The recognized gesture is displayed live on screen.

Recognized Gestures:

Gesture	Meaning
hello	Hello
thank_you	Thank You
mom	Mom
dada	Dada
me	Me
tanu	Tanu

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🗂️ Project Structure

Sign-Language-Gesture-Recognition/
│
├── src/
│   └── main.py                        # Real-time gesture recognition via webcam
│
├── model/
│   └── gesture_recognition_model.h5   # Trained Keras model
│
├── data/
│   ├── data.npy                        # Combined training data
│   ├── labels.npy                      # Training labels
│   ├── hello.npy                       # Gesture data — Hello
│   ├── thank_you.npy                   # Gesture data — Thank You
│   ├── mom.npy                         # Gesture data — Mom
│   ├── dada.npy                        # Gesture data — Dada
│   ├── me.npy                          # Gesture data — Me
│   └── tanu.npy                        # Gesture data — Tanu
│
├── requirements.txt
├── .gitignore
└── README.md

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⚙️ Setup & Installation

1. Clone the repository

git clone https://github.com/<your-org>/Sign-Language-Gesture-Recognition.git
cd Sign-Language-Gesture-Recognition

2. Create a virtual environment (recommended)

python -m venv venv
venv\Scripts\activate        # Windows
source venv/bin/activate     # Mac/Linux

3. Install dependencies

pip install -r requirements.txt

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🚀 How to Run

python src/main.py

• Allow webcam access when prompted
• Show your hand gesture in front of the camera
• The recognized gesture label appears on screen in green text
• Press Q to quit

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🧠 How It Works

1. Webcam captures each frame in real-time
2. MediaPipe Hands detects 21 hand landmarks (x, y coordinates)
3. Landmarks are passed to the Keras model for classification
4. The predicted gesture label is displayed on the frame
5. Hand connections drawn with green dots and red lines for visual feedback

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🛠️ Tech Stack

Tool	Details
Python	3.8.9
OpenCV	Real-time video capture
MediaPipe	Hand landmark detection
TensorFlow / Keras	Gesture classification model
NumPy	Data handling
Platform	PyCharm

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👩‍💻 Author

Tanuja Devi. M

📄 License

This project is for educational purposes.