• Sanjay KP
• Mithil Gunaga
• Manoj M
• Chinmayananda R M
• Prof. Sinchana

People who are deaf and also hearing often struggle to interact with mainstream information retrieval systems, which typically require text input or spoken language. This research introduces an inclusive digital solutions that helps users to perform searches using sign language gestures. The system combines the both computer vision, deep learning, and natural language processing to accurately interpret visual signs. A convolutional neural network (CNN) processes spatial features from the video feed, while a recurrent neural network (RNN) captures the temporal patterns of motion. Recognized hand gestures or signs are then translated into specific commands or search queries, which the system uses to access relevant information from either online APIs or internal databases. A user-friendly interface ensures accessibility on both desktop and mobile platforms. Testing demonstrates that the system effectively interprets common signs and delivers accurate results, offering improved access for users who rely on sign language.

Sign language recognition; information retrieval; convolutional neural network; accessibility; human–computer interaction; gesture recognition

"SIGN LANGUAGE BASED INFORMATION RETRIEVAL SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.448-451, July-2025, Available :http://www.jetir.org/papers/JETIRGX06083.pdf

"SIGN LANGUAGE BASED INFORMATION RETRIEVAL 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. pp448-451, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06083.pdf