Hand Gesture Recognition

Hand Gesture Recognition is a project that enables device control using natural hand movements. It utilizes Keras and TensorFlow to recognize motion gestures such as swipes, thumbs up, and thumbs down. This project aims to enhance user experience and accessibility in various applications and platforms.

Features

• Device Control: The project allows users to control devices using intuitive hand gestures.

• Gesture Recognition: It recognizes and interprets hand motions like swipes, thumbs up, and thumbs down.

Getting Started

These instructions will guide you through setting up and running the Hand Gesture Recognition project on your local machine.

1. Clone the repository:

git clone https://github.com/your-username/hand-gesture-recognition.git

2. Install the required dependencies:

pip install -r requirements.txt

3. Run the application:

python3 Neural\ Networks.py

Usage

• Launch the application on your device.
• Use your webcam to capture hand gestures.
• The application will recognize the gestures and perform corresponding actions.

Model Training

If you want to retrain the model or improve its accuracy, follow these steps:

1. Collect and preprocess a dataset of hand gesture images.
2. Use the provided code to train the gesture recognition model.
3. Save the trained model and update the application to use the new model.

Acknowledgments

• Keras
• TensorFlow

Contact

For any questions or feedback, please reach out to Tanmai Niranjan.

Enhance user experience with natural hand gestures!