Real-Time Sign Language Recognition System: Word Level

Communications are very important in the people community. This research paper aims to develop a real-time sign language detection system based on Recurrent Neural Networks (RNNs) that translates gestures into written words. To record the temporal dynamism involved in sign language, initially collecting and pre-processing a large dataset of different sign language motions, followed by designing and training an effective RNN model. A user-centric interface that would be accessible by individuals with various levels of technology expertise will be created to enhance usability and simplicity in order to integrate easily into everyday communication aids. The initiative focuses on testing with users and extensive testing and evaluation to ensure high accuracy and reliability, advancing deaf and hard-of-hearing inclusion and accessibility.