  Hand gesture recognition systems provide a natural way for humans to interact
with computer systems. Although various algorithms have been designed for this
task, a host of external conditions, such as poor lighting or distance from the
camera, make it difficult to create an algorithm that performs well across a
range of environments. In this work, we present GRLib: an open-source Python
library able to detect and classify static and dynamic hand gestures. Moreover,
the library can be trained on existing data for improved classification
robustness. The proposed solution utilizes a feed from an RGB camera. The
retrieved frames are then subjected to data augmentation and passed on to
MediaPipe Hands to perform hand landmark detection. The landmarks are then
classified into their respective gesture class. The library supports dynamic
hand gestures through trajectories and keyframe extraction. It was found that
the library outperforms another publicly available HGR system - MediaPipe
Solutions, on three diverse, real-world datasets. The library is available at
https://github.com/mikhail-vlasenko/grlib and can be installed with pip.
