Accessible Real-Time Chart Interpretation for the Visually Impaired: Bridging the Gap in Data Accessibility

This study addresses the growing need for real-time chart captioning systems that improve accessibility for visually impaired individuals in remote communication environments. As online education and virtual meetings increasingly rely on visual data such as charts and infographics, visually impaired users face challenges in accessing critical information. We propose an AI-driven system designed to interpret and generate real-time captions for various types of charts. The model integrates object detection, caption generation, and temporal tracking to provide accurate and responsive descriptions aligned with live presentations. Unlike traditional methods that rely on post-event captions or manual assistance, our system offers real-time support, enhancing users’ ability to participate in online discussions and decision-making processes. The generated captions are context-aware and optimized for auditory delivery, ensuring clarity and relevance. We evaluate the system using publicly available datasets and real-world lecture videos to validate its performance in terms of accuracy, latency, and user satisfaction. Our findings show that the model significantly improves accessibility without compromising performance. This research contributes to inclusive digital environments by bridging the gap in data comprehension for visually impaired users during remote communication.