# Using artificial-intelligence tools to make LaTeX content accessible to blind readers

arXiv: https://arxiv.org/abs/2306.02480 · HTML: https://mlsanity.com/paper/2306.02480/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2023.

## Built on by (most cited first, 3 of 3)

- [Multilingual Performance of a Multimodal Artificial Intelligence System on Multisubject Physics Concept Inventories](https://mlsanity.com/paper/2501.06143/) (2025)
- [TeXBLEU: Automatic Metric for Evaluate LaTeX Format](https://mlsanity.com/paper/2409.06639/) (2024)
- [MathReader : Text-to-Speech for Mathematical Documents](https://mlsanity.com/paper/2501.07088/) (2025)

## Abstract (arXiv)

Screen-reader software enables blind users to access large segments of electronic content, particularly if accessibility standards are followed. Unfortunately, this is not true for much of the content written in physics, mathematics, and other STEM-disciplines, due to the strong reliance on mathematical symbols and expressions, which screen-reader software generally fails to process correctly. A large portion of such content is based on source documents written in LaTeX, which are rendered to PDF or HTML for online distribution. Unfortunately, the resulting PDF documents are essentially inaccessible, and the HTML documents greatly vary in accessibility, since their rendering using standard tools is cumbersome at best. The paper explores the possibility of generating standards-compliant, accessible HTML from LaTeX sources using Large Language Models. It is found that the resulting documents are highly accessible, with possible complications occurring when the artificial intelligence tool starts to interpret the content.