论文标题
手语识别的全面审查：不同类型，方式和数据集
A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets
论文作者
论文摘要
机器可以理解人类的活动，迹象的含义可以帮助克服听不清和普通百姓之间的沟通障碍。手语识别（SLR）是一个引人入胜的研究领域，也是有关计算机视觉和模式识别的关键任务。最近，在许多应用程序中，SLR的使用率增加了，但是环境，背景图像分辨率，模式和数据集会极大地影响性能。许多研究人员一直在努力进行通用的实时SLR模型。这篇评论论文促进了SLR的全面概述，并讨论了与SLR相关的需求，挑战和问题。我们研究有关手册和非手术，各种方式和数据集的相关作品。在过去的十年中，研究进度和现有的最先进的SLR模型已经审查。最后，我们发现了该领域的研究差距和局限性，并提出了未来的方向。这篇评论论文将对读者和研究人员获得有关SLR的完整指导以及最先进的SLR模型的渐进设计
A machine can understand human activities, and the meaning of signs can help overcome the communication barriers between the inaudible and ordinary people. Sign Language Recognition (SLR) is a fascinating research area and a crucial task concerning computer vision and pattern recognition. Recently, SLR usage has increased in many applications, but the environment, background image resolution, modalities, and datasets affect the performance a lot. Many researchers have been striving to carry out generic real-time SLR models. This review paper facilitates a comprehensive overview of SLR and discusses the needs, challenges, and problems associated with SLR. We study related works about manual and non-manual, various modalities, and datasets. Research progress and existing state-of-the-art SLR models over the past decade have been reviewed. Finally, we find the research gap and limitations in this domain and suggest future directions. This review paper will be helpful for readers and researchers to get complete guidance about SLR and the progressive design of the state-of-the-art SLR model
