Abstract

Sign Language (SL) is the most organized and structured type of hand/arm gesture in the communicative hand/arm gesture taxonomies. The ability of machines to comprehend human actions and meanings has numerous uses. SL identification is one area of focus. SL is employed by the deaf and hard-of-hearing communities to communicate. Hearing-impaired persons communicate via visual indicators instead of vocal communication and sound patterns. SL also uses facial expressions and body postures as a medium of communication. Pattern matching, computer vision, natural language processing are the key factors in SL identification. This study reviews state-of-the-art methodologies employed in current SL identification research, comparing the various algorithms at each stage. Discuss the challenges and limitations of gesture identification research in general, as well as SL identification in particular. Overall, this paper gives a thorough introduction to the topic of automated SL identification, paving the way for future research.