International Journal of Science, Engineering and TechnologyAuthors: Tejas Shantaram Jadhav, Professor Bangar A.P., Professor Bhosale S.B., Dr. A. A. Khatri
Abstract: This paper reviews recent computer-vision approaches that convert sign language into text. Sign language is the main means of communication for people with hearing and speech impairments, yet most hearing people cannot understand it. Four recent IEEE Access papers are studied: a comprehensive survey of traditional and deep-learning recognition methods, a two-stream graph convolution network with attention and residual connections, a Vision Transformer for static Indian signs, and a lightweight pose-based graph convolution pipeline called SignGraph. The review compares their input modalities, models, datasets, accuracy and computational cost. The study shows that skeleton (pose) based methods built on MediaPipe-style landmarks give accuracy close to or better than RGB-based models while needing about twenty times fewer parameters, which suits real-time webcam use. Open problems such as large vocabularies, signer independence, non-manual features and the scarcity of Indian Sign Language data are discussed, and directions for future work are suggested.
