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

GRLib: An Open-Source Hand Gesture Detection and Recognition Python Library