Advancements and challenges in vision-based sign language recognition: A comprehensive review

The latest developments in computer vision have led to a significant evolution in motion identification and gesture recognition techniques. Recent advancements in Sign Language Recognition (SLR) have facilitated communication between computer systems and humans. The development of a real-time computerized Sign Language (SL) identification system will be an invaluable asset for individuals with hearing and speech impairments, facilitating their connection with the world. Despite numerous research efforts in the field of SLR, the development of a real-time autonomous SLR framework remains essential. The strategies employed may possess advantages and disadvantages relative to other methodologies, and they differ among researchers. Despite several research efforts aimed at identifying optimal techniques and models for SLR, challenges persist in consistently using various SLR models and procedures. The availability of the designed automated SLR system as a product is accompanied by increased costs and resource challenges. Therefore, researchers continue to seek a profitable solution that benefits the hearing-impaired. This paper highlights the challenges encountered by literature in creating a successful model for the hearing and speech-impaired society. This study focuses on the introduction of SL identification techniques utilizing algorithms, particularly in recent years. It encompasses recognition models based on conventional approaches and deep learning techniques, as well as SL datasets and barriers. We synthesize these insights to highlight key trends and research gaps, offering a roadmap for future innovations in scalable, inclusive SLR systems.