Identifying the best web accessibility workflows for legacy archival description data

This paper examines the challenges and solutions associated with making archival PDF finding aids accessible to blind and low-vision users, particularly those who rely on screen readers. The project, conducted at the University of Toronto, highlights the barriers posed by unstructured PDFs, which fail to meet the various accessibility standards specified in the WWW Consortium’s Web Content Accessibility Guidelines. Three methods were tested to improve accessibility: manual remediation, PDF-to-HTML conversion, and data migration into the Access to Memory (AtoM) platform. The results indicated that both the manual and automated remediation methods were either too costly or ineffective, while the most promising approach involved migrating the description data into AtoM via CSV import, enhancing both accessibility and search functionality. The paper underscores the need for ongoing funding and professional expertise to address web accessibility issues in archival settings and outlines future steps to improve PDF generation within AtoM for broader application.