A comprehensive survey and taxonomy of sign language research

Sign language relies on visual gestures of human body parts to convey meaning and plays a vital role in modern society to communicate and interact with people having hearing difficulty as well as for human- machine interaction applications. This field has attracted a growing attention in recent years and several research outcomes have been witnessed covering various issues including sign acquisition, segmentation, recognition, translation and linguistic structures. In this paper, a comprehensive up-to-date survey of the state -of-the-art literature of automated sign language processing is presented. The survey provides a taxonomy and review of the body of knowledge and research efforts with focus on acquisition devices, available databases, and recognition techniques for fingerspelling signs, isolated sign words, and continuous sentence recognition systems. It covers recent advances including deep machine learning and multimodal approaches and discusses various related challenges. This survey is directed to junior researchers and industry developers working on sign language gesture recognition and related systems to gain insights and identify distinctive aspects and current status of existing landscape as well as future perspectives leading to further advancements.

Automated summary

Sign language is crucial in modern society for communication with people who have hearing difficulties. Recent research in automated sign language processing covers a wide range of topics including sign acquisition, recognition, and translation, utilizing technologies such as deep machine learning and multimodal approaches.