Ai-Based Sign Language to Text Translator Using Computer Vision and Deep Learning

Over half a million people in the United States use American Sign Language (ASL), yet only a fraction of the general population understands it, creating significant communication barriers. This paper introduces an innovative, real time American Sign Language Translation system, designed to bridge those communication barriers between ASL users and non-signers. Integrating computer vision (CV), deep learning, and natural language processing (NLP), we successfully can capture, recognize, and translate ASL movements into coherent textual output. Landmark training extraction and spatial-temporal modeling with hybrid architectures led to the team conceptualizing a unique combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). Additionally, recognizing ASL's contextual nature [15], Large Language Models (LLM) trained with NLP are used to reason out sentences. Extensive evaluation demonstrated a $\mathbf{9 2 \%}$ average accuracy in gesture classification, robust performance under varied environmental conditions (lighting, hand sizes, gesture speed), and real time applicability. This research highlights the urgent need for improved accessibility standards, empowering ASL users toward greater independence. Future work could further integrate this technology into broadcast media, enhance gesture recognition speed, and extend translations to additional sign languages.