SIGN-AI: An AI Assistant for Sign Language

This paper introduces SIGN-AI, a unified framework for high-fidelity sign language synthesisvia text, voice, and video Inputs, it is a transformative framework for real-time sign language synthesis addressing critical accessibility gaps for 70 million deaf individuals worldwide.Confronting significant barriers—including 83% lack of accessible STEM education materials,only 4.7% online content with sign interpretation, and 3× higher unemployment rates—wepropose two novel algorithmic approaches: Algorithm 1 (Text/Image-Driven Synthesis)employs a modular pipeline converting multimodal inputs to text, generating sign imagesvia Vision Transformers trained on SGN-IMG datasets, animating frames through opticalflow prediction (0.5-2s clips), and sequencing with LSTM models. This approach deliverscomputational efficiency (CPU-feasible) and explicit sign control. Algorithm 2 (End-to-End Video Synthesis) utilizes diffusion models trained on SGN-VID datasets to directlytranslate text into motion vectors, producing 1-3s sign segments with attention-based fusion,achieving superior motion fidelity through learned co-articulation.Benchmarks demonstrate70% resource reduction (Algorithm 1) and 92% motion naturalness (Algorithm 2) versus state-of-the-art. Projected impacts include: 45% faster STEM concept acquisition, 30% increasein deaf graduates by 2035, and $17B annual economic productivity gain. The frameworkenables real-time translation of streaming media, educational content, and professionalcommunications, with future research targeting low-latency video-to-video conversion andan AI Assistant.