手势识别系统为人类与计算机系统交互提供了一种自然的方式。尽管针对此任务设计了各种算法，但许多外部条件（例如光线不足或距相机的距离）使得创建在各种环境中表现良好的算法变得困难。在这项工作中，我们提出了 GRLib：一个开源 Python 库，能够检测和分类静态和动态手势。此外，该库可以根据现有数据进行训练，以提高分类的鲁棒性。所提出的解决方案利用 RGB 相机的反馈。然后，检索到的帧经过数据增强并传递到 MediaPipe Hands 以执行手部标志检测。然后将这些地标分类到它们各自的手势类别中。该库通过轨迹和关键帧提取支持动态手势。结果发现，该库在三个不同的真实数据集上的性能优于另一个公开可用的 HGR 系统 - MediaPipe Solutions。该库可从 https://github.com/mikhail-vlasenko/grlib 获取，并且可以使用 pip 安装。 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.