Asma Khan

Sign language utilizing hand gestures for visual mode of communication, body actions, and facial expressions. Due to the increasing incidence of hearing deficiencies, the field of Continuous Sign Language Recognition (CSLR) has seen a considerable increase in research, which involves identifying consecutive signs in video streams without previous information of their sequential limitations. This survey reviews CSLR research, presenting deep knowledge into the development of CSLR systems. It critically analyzes numerous studies, organizing them into a comprehensive taxonomy covering aspects such as sign language, data collection, input method, gesture signals, identification methods, applied data collections, and comprehensive efficiency. The article further categorizes deep-learning CSLR models according to spatial, temporal, and alignment approaches, highlighting their benefits and drawbacks. Furthermore, it explores various research aspects, such as the challenges of CSLR, the significance of nonverbal elements in CSLR systems, and the gaps in the body of current research. This classification serves as a helpful tool for researchers developing and organizing cutting-edge CSLR methods. The study highlights the effectiveness of deep learning systems capture different sign language signals. On the other hand, several challenges remain, such as the need for diverse, naturalistic datasets, improved signer diversity, and realtime CSLR systems. Addressing these gaps will be essential for advancing CSLR’s real-world applications and developing more robust, efficient models for the future. The conclusions give a wider Comprehension of sign language recognition and set the groundwork for future studies focused on addressing the current challenges and issues in this developing area.