Easy Reads: A Python program for making Scientific Papers on arXiv more Reader Friendly and Accessible

2026-06-18

AI Summary

Scientific papers often suffer from poor readability and limited accessibility due to small font sizes, narrow line spacing, double-column layouts, and dense figures. To address this, this work proposes an end-to-end open-source Python tool that, for the first time, automatically retrieves LaTeX source code of papers from arXiv and reformats them according to user-specified preferences—such as font size and number of columns. By integrating LaTeX parsing with layout rewriting techniques, the method substantially enhances reading comfort and accessibility while striking an effective balance between automated processing and customizable personalization.

Abstract

Scientific papers are frequently dense and characterized by features such as small fonts and line spacing, double columns of text, and tightly arranged figures. While these features make papers more compact, they can hinder readability, make them less accessible, and can strain the reader. arXiv is a premier open-access repository for scientific papers across different fields and is used extensively by researchers, including those in the physics and astrophysics communities. Easy Reads is an automated, end-to-end, open-source Python program that helps address the stated challenge by making papers from arXiv more reader-friendly and accessible. Easy Reads can automatically fetch a paper from arXiv via its URL and work with the source TeX file to allow custom formatting of the paper features, primarily the font size, and the number of columns used. The main goal of Easy Reads is to facilitate ease of reading of scientific papers.

Problem

• readability
• accessibility
• scientific papers
• arXiv
• document formatting

Innovation

• Easy Reads
• arXiv
• TeX processing
• accessibility
• automated formatting