  This poster addresses accessibility issues of electronic theses and
dissertations (ETDs) in digital libraries (DLs). ETDs are available primarily
as PDF files, which present barriers to equitable access, especially for users
with visual impairments, cognitive or learning disabilities, or for anyone
needing more efficient and effective ways of finding relevant information
within these long documents. We propose using AI techniques, including natural
language processing (NLP), computer vision, and text analysis, to convert PDFs
into machine-readable HTML documents with semantic tags and structure,
extracting figures and tables, and generating summaries and keywords. Our goal
is to increase the accessibility of ETDs and to make this important scholarship
available to a wider audience.
