Hand Sign Detection using TensorFlow Lite and Flask

This project demonstrates hand sign detection using TensorFlow Lite and Flask. It captures video frames, performs hand sign detection using a pre-trained TensorFlow Lite model, adds a unique number to the frame, and displays the processed frames in a web interface using Flask.

Features

• Capture video frames and perform hand sign detection.
• Display processed frames in a web interface using Flask.
• Add unique identifiers to each processed frame.
• Store processed frames on the server.

Getting Started

Prerequisites

• Python 3.7 or higher
• Virtualenv (optional but recommended)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/hand-sign-detection.git
   cd hand-sign-detection

2. (Optional) Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate

3. Install the required dependencies from requirements.txt:

   Copy code
   pip install -r requirements.txt

Running the Application

1. Start the Flask application:

python app.py

2. Open your web browser and navigate to http://127.0.0.1:5000 to access the web interface.

3. Follow the instructions on the web interface to capture and process hand sign frames.

Usage

• Follow the on-screen instructions to capture video frames.
• The processed frames will be displayed with unique numbers added.
• You can view the stored processed frames in the processed_frames folder.

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow the guidelines in CONTRIBUTING.md.

License

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