Sign Language Recognition using LSTM and Media Pipe
G. Mallikarjuna Rao, Cheguri Sowmya, Dharavath Mamatha, P. A. Sujasri, S. Anitha, Ramavath Alivela
Abstract
There are learning aids available for those who are deaf or have trouble speaking or hearing, but they are rarely used. Live sign motions would be handled via image processing in the suggested system, which would operate in real-time. Classifiers would then be employed to distinguish between distinct signs, and the translated output would show text. On the set of data, machine learning algorithms will be trained. With the use of effective algorithms, top-notch data sets, and improved sensors, the system aims to enhance the performance of the current system in this area in terms of response time and accuracy. Due to the fact that they solely employ image processing, the current systems can identify movements with considerable latency. In this project, our research aims to create a cognitive system that is sensitive and reliable so that persons with hearing and speech impairments may utilize it in day-to-day applications