This project uses MediaPipe hand tracking and a neural network to classify American Sign Language (ASL) hand signs from a webcam video feed.

• Real-time hand tracking using MediaPipe
• Hand sign classification using a trained TensorFlow model
• Custom model trained on extracted hand landmarks (63 features)
• Supports one-hot encoded label training with image folders

• Python 3.11
• pip (Python package installer)
• A webcam (for real-time detection)

git clone https://github.com/junaid-pathan/signlanguage.git
cd signlanguage

If you do not have 3.11, create a virtual environment using this in your command prompt
# Linux/macOS
python3.11 -m venv venv
source venv/bin/activate

# Windows
python3.11 -m venv venv
venv\Scripts\activate


DOWNLOAD ALL THE REQUIRED PACKAGES BY RUNNING : pip install -r requirements.txt

FINALLY RUN THE modeL_for_gesture.py