🖐 Hand Gesture Recognition System

A professional real-time Hand Gesture Recognition System developed using Machine Learning, OpenCV, MediaPipe, and Streamlit as part of the SkillCraft Technology Machine Learning Internship.

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🚀 Features

• Real-time hand gesture recognition
• Live webcam detection
• Upload video support
• MediaPipe hand landmark tracking
• Detection history tracking
• Professional Streamlit UI
• Machine Learning gesture classification

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🛠 Technologies Used

• Python
• OpenCV
• MediaPipe
• Streamlit
• Scikit-learn
• NumPy
• Pandas

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📸 Project Screenshots

🏠 Main Interface

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✋ Live Hand Detection

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📹 Video Upload Detection

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▶ How to Run the Project

Step 1 — Clone Repository

git clone https://github.com/Saikishorep15/SCT_ML_4.git

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Step 2 — Install Dependencies

pip install -r requirements.txt

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Step 3 — Run Streamlit App

python -m streamlit run app.py

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📊 Model Details

• Algorithm: Random Forest Classifier
• Dataset: LeapGestRecog
• Accuracy Achieved: 100%

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🌟 Future Improvements

• Sign language recognition
• Gesture-based virtual mouse
• Voice assistant integration
• Advanced deep learning models

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📂 Project Structure

SCT_ML_4/
│
├── dataset/
├── images/
│   ├── image1.png
│   ├── image2.png
│   └── image3.png
│
├── app.py
├── train_model.py
├── gesture_model.pkl
├── README.md
└── requirements.txt

# 👨‍💻 Author

**SaiKishore P**

SkillCraft Technology Machine Learning Internship

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# 🔗 GitHub Repository

https://github.com/Saikishorep15/SCT_ML_4

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# 📌 Internship Task

Task 4 — Develop a hand gesture recognition model that can accurately identify and classify different hand gestures from image or video data.