Status: published
People with hearing and speech disabilities primarily rely on sign language to express their thoughts and interact with others. However, the lack of understanding of sign language among the general population often creates communication barriers. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies in bridging this gap through automated sign language recognition systems. This paper presents a comprehensive review of existing approaches, including image-based and video-based recognition models, deep learning techniques such as CNNs and LSTMs, and sensor-based systems. It also discusses major challenges such as dataset limitations, real-time performance, and gesture variability. Finally, it highlights potential future directions for developing robust and scalable sign language recognition systems.
Keywords: Sign Language Recognition (SLR), communication barrier, Deaf and hard-of-hearing community, Gesture based interaction, Image and video processing, Hand gesture analysis, Visual communication system.