Breaking Barriers: Real-Time Sign Language Recognition Using LSTM Networks for Enhanced Communication Accessibility

This paper presents an innovative approach to real-time sign language recognition using Long Short-Term Memory networks (LSTM), aimed at enhancing communication accessibil-ity for the deaf and hard-of-hearing community. We address the challenge of understanding and interpreting sign language, which is critical for millions worldwide, yet restricted to those proficient in it. Our research contributes to bridging this communication gap by developing a deep learning model capable of recognizing a broad spectrum of sign language gestures and sentences with high accuracy and speed. Utilizing a rich dataset comprising diverse sign language gestures, collected in collaboration with a professional video production studio and proficient sign language users, we employ LSTM networks integrated with Dense layers to effectively capture the complex spatial and temporal patterns of sign language. The architecture of our model is specifically designed to accommodate the nuanced dynamics of sign language, with an emphasis on real-time processing. Through rigorous training and validation, our model demonstrates an outstanding accuracy rate of 92 % on a comprehensive testing dataset, alongside remarkable real-time processing capabilities. The sys-tem's efficiency in recognizing a wide array of sign gestures nearly instantaneously underscores its potential applicability in various real-world scenarios, including assistive technologies and human-computer interaction. This study not only showcases the practicality and efficacy of LSTM networks in real-time sign language recognition but also marks a significant step towards more inclusive and accessible communication technologies. Our future work includes integrating this system with the Langue des Signes Quebecoise website, further advancing the goal of universal communication accessibility.