ADVANCEMENT IN SIGN LANGUAGE RECOGNITION TECHNOLOGIES: A COMPREHENSIVE REVIEW

This comprehensive review paper delves into the intricate landscape of sign language recognition (SLR) technologies, addressing the pressing need for effective communication solutions for the global population grappling with hearing impairments. With a meticulous examination of the past two decades of SLR research, the paper categorically explores isolated and continuous manual and non-manual SLR, shedding light on diverse modalities and datasets. The review not only emphasizes the significance of sign language as a fully developed language but also underscores the potential of advanced technologies, such as deep learning and unsupervised learning, in revolutionizing SLR. The inclusion of diverse research projects, ranging from real-time Indian Sign Language conversion to text to the development of deep learning models for American Sign Language recognition, adds practical depth to the exploration. The paper's contributions extend to presenting a tabular format for enhanced comprehension, addressing key facets like modality and sign language types, and offering insightful guidance for the trajectory of this burgeoning field. Overall, this review serves as a valuable and timely resource, advocating for continued innovation to foster a more inclusive and interconnected world for individuals with hearing impairments.