Artificial intelligence in sign language recognition: A comprehensive bibliometric and visual analysis

Abstract

Sign language recognition (SLR) plays a crucial role in bridging the communication gap between individuals with hearing impairments and the auditory communities. This study explores the use of artificial intelligence (AI) in SLR through a comprehensive bibliometric analysis of 2,720 articles published from 1988 to 2024. Utilizing tools like VOSviewer and CiteSpace, the research uncovers the landscape of publication outputs, influential articles, leading authors, as well as the intellectual framework of current topics and emerging trends. The findings indicate that since the inception of SLR research in 1988, there has been a rapid expansion in the field, particularly from 2004 onwards. China and India lead in research productivity. Keyword and co-citation analyses highlight that Hidden Markov Model, Kinect, and Deep Learning have been focal points at various stages of SLR development, while transfer learning, Bidirectional Long Short-Term Memory, attention mechanisms, and Transformer models represent recent emerging trends. This research offers valuable insights for scholars and practitioners interested in AI-based SLR.