• Mr.Vitraag Shete
• Mr.Yogeshwar Prabhu
• Ms.Kusuma
• Ms.Sameeksha
• Prof.Rithika

Indian Sign Language (ISL) plays a vital role in bridging communication gaps between the hearing-impaired community and the hearing population in India. However, widespread technological support for automated ISL translation is limited, which hampers effective communication and inclusion. This paper presents an exhaustive survey of contemporary ISL translating systems, emphasizing both vision-based and sensor-based methodologies. We thoroughly analyze various machine learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid architectures, which have been applied for spatial and temporal feature extraction from ISL gestures. This study discusses the challenges faced by current systems such as dataset insufficiency, signer variability, and the complexity of dynamic sign recognition. Moreover, we explore emerging trends and future research directions aimed at developing scalable, accurate, and user-friendly ISL translation solutions. Our work provides a comprehensive foundation for researchers and practitioners interested in advancing ISL recognition technology to enhance social integration and accessibility for the deaf community in India.

Indian Sign Language, Gesture Recognition, Deep Learning, Assistive Technology, Human-Computer Interaction

"A SURVEY ON INDIAN SIGN LANGUAGE TRANSLATING SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.553-557, July-2025, Available :http://www.jetir.org/papers/JETIRGX06107.pdf

"A SURVEY ON INDIAN SIGN LANGUAGE TRANSLATING SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp553-557, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06107.pdf