This repository contains code to develop a hand gesture recognition model that can accurately identify and classify different hand gestures from image or video data. The goal is to enable intuitive human-computer interaction and gesture-based control systems.
The model is built using deep learning techniques and trained on the LeapGestureRecog dataset, which can be found at "https://www.kaggle.com/datasets/gti-upm/leapgestrecog". This dataset consists of images of hand gestures captured using the Leap Motion Controller.
To run the code in this repository, you will need the following dependencies:
Python (>=3.6)
TensorFlow (>=2.0)
Keras (>=2.3)
NumPy
Matplotlib
OpenCV
LeapGestureRecog dataset contributors
Kaggle for hosting the dataset