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

arXiv: https://arxiv.org/abs/2310.14919 · HTML: https://mlsanity.com/paper/2310.14919/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [MediaPipe Hands: On-device Real-time Hand Tracking](https://mlsanity.com/paper/2006.10214/) (2020)
- [On-device Real-time Hand Gesture Recognition](https://mlsanity.com/paper/2111.00038/) (2021)
- [Dynamic Hand Gesture Recognition for Wearable Devices with Low Complexity Recurrent Neural Networks](https://mlsanity.com/paper/1608.04080/) (2016)
- [Gestop : Customizable Gesture Control of Computer Systems](https://mlsanity.com/paper/2010.13197/) (2020)

## Abstract (arXiv)

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.