GTC-Hand-Gesture-Recognition

This project is an AI-based system designed to recognize hand gestures from static images or live video streams. It aims to enable touchless control and enhance applications such as human-computer interaction, gaming, and sign language interpretation.

ScreenShots

Features

• Real-time hand gesture recognition via webcam
• Training and evaluation scripts with clear CLI entry points
• Pretrained MobileNetV2 weights included for quick demo
• Results artifacts: confusion matrix and classification report

🚀 Quickstart

1- Fork and Clone repo

• git clone https://github.com/24-mohamedyehia/GTC-Hand-Gesture-Recognition

2- 📦 Install Python Using Miniconda

• Download and install MiniConda from here

3- Create a new environment using the following command:

conda create --name Hand-Gesture-Recognition python=3.11 -y

4- Activate the environment:

conda activate Hand-Gesture-Recognition

5- Install the required packages

pip install -r requirements.txt

6- Setup the environment variables

cp .env.example .env

Demo Video

• Watch a sample demo locally: Hand_Gesture_recognition.mp4
• Tip: GitHub may not stream MP4 in-browser. If playback fails, download the file or convert to MP4 for embedding.

Presentation

• Canva : Presentation (Canva)
• Powerpoint : Presentation (Powerpoint)

Dataset

We used the ASL Alphabet dataset for training and evaluating our hand gesture recognition model. You can download the dataset from Kaggle

Project Structure

GTC-Hand-Gesture-Recognition/
│
├── Data/                      # Dataset root (train/val)
│   ├── train/
│   ├── test/
│   └── val/
│
│
├── preprocessing/           # Data cleaning & augmentation scripts
│   ├── dataloader.py
│   ├── transforms.py
│   └── __init__.py
│
├── models/                  # Architectures & model builders
│   ├── mobilenet.py         # MobileNetV2 backbone
│   └── __init__.py
│
├── scripts/                 # Training / Evaluation / Prediction
│   ├── train.py
│   ├── evaluate.py
│   ├── predict_simple.py
│   ├── predict_realtime.py
│   └── split_data.py
├── outputs/                 # Saved results (checkpoints, plots, logs)
│   ├── classification_report.csv
│   ├── confusion_matrix.png
│   └── mobilenetv2_gesture.pth
│
├── utils/                   # Helper functions       
│   ├── __init__.py
│   ├── metrics.py
│   └── train_utils.py
│
├── deployment/              # Apps / serving
│   └── app.py              
│
├── labels.json              # Mapping: class ↔ index
├── requirements.txt         # Python dependencies
├── .env.example        
├── README.md                # Main documentation
└── .gitignore

Technical Stack

• Python 3.11
• Pytorch
• OpenCV
• streamlit
• Scikit-learn

Authors

• Mohamed Yehia
• Ahmed Ammar
• Faris Abouagour
• Mohamed Ghoneim
• Youssef Fady

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

📝 Acknowledgments

• ASL Alphabet Dataset
• GTC