Reviewing 25 years of continuous sign language recognition research: Advances, challenges, and prospects

Abstract

Sign language is a form of visual communication employing hand gestures, body movements, and facial expressions. The growing prevalence of hearing impairment has driven the research community towards the domain of Continuous Sign Language Recognition (CSLR), which involves identification of successive signs in a video stream without prior knowledge of temporal boundaries. This survey article conducts a review of CSLR research, spanning the past 25 years, offering insights into the evolution of CSLR systems. A critical analysis of 126 studies is presented and organized into a taxonomy comprising seven critical dimensions: sign language, data acquisition, input modality, sign language cues, recognition techniques, utilized datasets, and overall performance. Additionally, the article investigated the classification of deep-learning CSLR models, categorizing them based on spatial, temporal, and alignment methods, while identifying their advantages and limitations. The article also explored various research aspects including CSLR challenges, the significance of non-manual features in CSLR systems, and identified gaps in existing literature. This literature taxonomy serves as a resource aiding researchers in the development and positioning of novel CSLR techniques. The study emphasizes the efficacy of multi-modal deep learning systems in capturing diverse sign language cues. However, the examination of existing research uncovers numerous limitations, calling for continued research and innovation within the CSLR domain. The findings not only contribute to the broader understanding of sign language recognition but also lay the foundations for future research initiatives aimed at addressing the persistent challenges within this emerging field.