Enhancing Accessibility with LSTM-Based Sign Language Detection

Abstract

Sign language serves as a vital means of communication for the deaf and hard of hearing community. However, identifying sign language poses a significant challenge due to its complexity and the lack of a standardized global framework. Recent advances in machine learning, particularly Long Short-Term Memory (LSTM) algorithms, offer promise in the field of sign language gesture recognition. This research introduces an innovative method that leverages LSTM, a type of recurrent neural network designed for processing sequential input. Our goal is to create a highly accurate system capable of anticipating and reproducing sign language motions with precision. LSTM's unique capabilities enhance the recognition of complex gestures by capturing the temporal relationships and fine details inherent in sign language. The results of this study demonstrate that LSTM-based approaches outperform existing state-of-the-art techniques, highlighting the effectiveness of LSTM in sign language recognition and their potential to facilitate communication between the deaf and hearing communities.