Visual hand gesture recognition with deep learning: a comprehensive review of methods, datasets, challenges and future research directions

The rapid evolution of deep learning (DL) models and the ever-increasing size of available datasets have raised the interest of the research community in the always-important field of visual hand gesture recognition (VHGR), and delivered a wide range of applications, such as sign language understanding and human-computer interaction using cameras. Despite the large volume of research works in the field, a structured and complete survey on VHGR is still missing, leaving researchers to navigate through hundreds of papers in order to find the current state-of-the-art (SOTA). The current survey aims to fill this gap by presenting a comprehensive overview of this computer vision field. With a systematic research methodology that identifies the SOTA works and a structured presentation of the various methods, datasets, and evaluation metrics, this review aims to constitute a useful guideline for researchers, helping them to propose improvements. Specifically, this survey focuses on four fundamental questions: what are the main VHGR aspects, what are the current SOTA methods, what comparative insights can be drawn across methods and tasks, and which challenges shape future research. Starting with the methodology used to locate the related literature, the survey identifies and organizes the key VHGR approaches in a taxonomy-based format, and presents the various dimensions that affect the final method choice, such as input modality, task type, and application domain. The SOTA techniques are grouped across three primary VHGR tasks: static, isolated dynamic and continuous gesture recognition. For each task, the architectural trends and learning strategies are listed. To support the experimental evaluation of future methods in the field, the study reviews commonly used datasets and presents the standard performance metrics. Our survey concludes by identifying the major challenges in VHGR, including both general computer vision issues and domain-specific obstacles, and outlines promising directions for future research.