PDF inaccessibility is an ongoing challenge that hinders individuals with visual impairments from reading and navigating PDFs using screen readers.This paper presents a step-by-step process for both novice and experienced users to create accessible PDF documents, including an approach for creating alternative text for mathematical formulas without expert knowledge.In a study involving nineteen participants, we evaluated our prototype PAVE 2.0 by comparing it against Adobe Acrobat Pro, the existing standard for remediating PDFs.Our study shows that experienced users improved their tagging scores from 42.0% to 80.1%, and novice users from 39.2% to 75.2% with PAVE 2.0.Overall, fifteen participants stated that they would prefer to use PAVE 2.0 in the future, and all participants would recommend it for novice users.Our work demonstrates PAVE 2.0's potential for increasing PDF accessibility for people with visual impairments and highlights remaining challenges. CCS Concepts• Human

Abstract Academic articles are commonly published in Portable Document Format (PDF). However, for many people with visual impairments, PDF formats present significant accessibility issues. This study addresses two research questions: 1) To what extent are PDFs in prominent academic repositories accessible? and 2) To what extent are accessibility issues in academic articles known and addressed by repositories? To answer these questions, 8,000 PDFs from four prominent repositories (Springer, Elsevier, ACM, and Wiley) were retrieved and were automatically analysed according to accessibility criteria based on the Matterhorn Protocol. Additionally, a quantitative content analysis was performed on the submission guidelines of repositories to determine the degree to which accessibility is considered in document creation. Results suggest that most PDFs were not tagged in spite of the fact that some repositories included accessibility in their general author guidelines. This paper concludes with recommendations to improve the accessibility of papers in academic repositories.

People with visual impairments use assistive technology, e.g., screen readers, to navigate and read PDFs. However, such screen readers need extra information about the logical structure of the PDF, such as the reading order, header levels, and mathematical formulas, described in readable form to navigate the document in a meaningful way. This logical structure can be added to a PDF with tags. Creating tags for a PDF is time-consuming, and requires awareness and expert knowledge. Hence, most PDFs are left untagged, and as a result, they are poorly readable or unreadable for people who rely on screen readers. STEM documents are particularly problematic with their complex document structure and complicated mathematical formulae. These inaccessible PDFs present a major barrier for people with visual impairments wishing to pursue studies or careers in STEM fields, who cannot easily read studies and publications from their field. The goal of this Ph.D. is to apply artificial intelligence for document analysis to reasonably automate the remediation process of PDFs and present a solution for large mathematical formulae accessibility in PDFs. With these new methods, the Ph.D. research aims to lower barriers to creating accessible scientific PDFs, by reducing the time, effort, and expertise necessary to do so, ultimately facilitating greater access to scientific documents for people with visual impairments.