Sign Comm: A Real-Time Indian Sign Language Recognition System Using Deep Learning for inclusive communication

Individuals who are deaf often utilize sign language for communication. In Sign Language, the symbols or motions are arranged grammatically. Every motion is referred to as a sign. Indian Sign Language Recognition using Deep Learning model describes the creation of SignComm, which improves communication for those with hearing loss by translating Indian Sign Language (ISL) motions into text. To identify and interpret ISL gestures in real-time. The paper describes the difficulties the deaf community faces due to the dearth of real-time sign language interpretation services. It suggests a method that uses an Automatic Sign Detection system with deep learning models. The research describes current systems and their shortcomings, such as the lack of real-time translation tools and the difficulties the deaf community faces in obtaining sign language interpretation. Next, it presents the suggested system, SignComm, which has benefits including quick sign language decoding, a user-friendly platform that is easy to use and can foster an inclusive atmosphere in public areas and educational institutions a literature review that examines several deep learning models and their applicability to the project is included in the paper. It emphasizes the potential of the ResNet-50 architecture and the significance of a unique ISL dataset. The benefits of the suggested method, including its accuracy, inclusivity, and capacity to address communication barriers, are highlighted in the paper. Additionally, it outlines the steps involved in the implementation process, such as gathering data, training the ResNet model, and deploying in real time. A user-friendly experience for converting ISL gestures into text is the main goal of the intricate user interface design.