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🖐 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.

🚀 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

🛠 Technologies Used

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

📸 Project Screenshots

🏠 Main Interface

Main Interface

✋ Live Hand Detection

Hand Detection

📹 Video Upload Detection

Video Upload

▶ How to Run the Project

Step 1 — Clone Repository

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

Step 2 — Install Dependencies

pip install -r requirements.txt

Step 3 — Run Streamlit App

python -m streamlit run app.py

📊 Model Details

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

🌟 Future Improvements

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

📂 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.