Publishing
Digital Accessibility
In the digital age, the Portable Document Format (PDF) remains a cornerstone for information dissemination across numerous sectors. From research papers and journals to textbooks, course materials, and administrative documents, PDFs facilitate the widespread sharing of knowledge and information. However, the utility and reach of these documents are significantly limited if they are not accessible to all users, including individuals with disabilities who rely on assistive technologies such like screen readers, braille displays, and alternative input devices. PDF accessibility is not only an ethical responsibility but increasingly a legal requirement under regulations like the European Accessibility Act (EAA 2025), Section 508 (U.S.), the UK Public Sector Bodies Accessibility Regulations, and Australia’s Disability Discrimination Act. Nearly every country is moving forward on digital accessibility legislation, making non-compliance a legal risk and a barrier to inclusive access.
Accessibility compliance, governed by standards such as WCAG (Web Content Accessibility Guidelines) and PDF/UA (PDF/Universal Accessibility), and, mandated by laws such as the European Accessibility Act (EAA 2025), Section 508, and others necessitates that digital documents are structured logically and semantically. This is achieved for PDF through a process known as Tagging, where structural elements like headings, paragraphs, lists, tables, figures, and their associated alternative text are programmatically identified and ordered. For assistive technologies to accurately interpret and convey the document's content and navigation, this underlying tag structure is indispensable.
PDF output from modernized typesetting workflows can be created “born-digital” and properly tagged for accessibility if the creator, vendor or publisher, has developed proper workflows. Increasingly fresh content is born-digital and can be created accessible.
If a publisher or commercial organization has limited control over the PDF creation process, or if they have a collection of already created PDF, remediation of the PDF file to become accessible is a necessity. Often teams resort to using very manual processes to create tagged PDF.
The traditional approach to PDF tagging is predominantly manual. This involves human operators using specialized software to navigate through the document, identify each meaningful element, determine its function, and apply the corresponding tag. The process is inherently time-consuming, requires a deep understanding of accessibility standards and tagging best practices, and is prone to human error, especially when dealing with complex layouts, intricate tables, embedded equations, or high volumes of documents. For institutions and publishers managing vast archives and generating copious new content regularly, the manual effort associated with achieving and maintaining PDF accessibility is substantial, leading to significant resource allocation and potential delays in making content available. The sheer scale of remediation needed to address legacy documents and ensure ongoing compliance with new publications presents a formidable challenge.
This is where Artificial Intelligence, specifically platforms like ADAPT AI, offers a transformative solution. ADAPT AI is engineered to automate the intricate process of PDF tagging, dramatically reducing the reliance on manual effort. By leveraging advanced machine learning algorithms and natural language processing, ADAPT AI can analyze the visual presentation and underlying structure of a PDF to automatically identify and tag various document elements with remarkable accuracy.
The platform's AI engine is trained on diverse datasets, enabling it to recognize patterns and structures indicative of specific content types across a wide range of document layouts commonly found in publishing and educational materials. This includes accurately identifying hierarchical heading levels, distinguishing between different types of lists, recognizing and structuring complex tables, and identifying figures that require alternative text descriptions, and inserting alt-text for human review. Furthermore, ADAPT AI is adept at determining the correct reading order of content, a critical factor for ensuring a coherent experience for users of screen readers.
Critically, ADAPT AI includes a built-in PDF Accessibility QA automation step before human review. It runs documents through accessibility checkers to flag potential structural or semantic issues. This early QA pass highlights errors or inconsistencies for the human reviewer, making final validation more efficient and less error-prone.
The core of ADAPT AI's ability to deliver a reported 70% reduction in manual effort lies in its capacity to generate a comprehensive, accurate baseline of tags automatically. Instead of human operators having to manually identify and apply tags to every single element, ADAPT AI performs this initial, labor-intensive step with high efficiency. Automated accessibility QA reports are generated alongside the initial tagging process, providing a roadmap for where human validation is required. The role of the human expert then shifts from primary tag application to a supervisory and refinement capacity. This involves reviewing the AI-generated tags, validating their accuracy, and making necessary adjustments for highly complex or ambiguous elements that may require nuanced human interpretation. This division of labor, where AI handles the bulk of the repetitive work and human expertise is focused on quality assurance and complex problem-solving, fundamentally changes the economics and timelines of PDF accessibility.
Consider a scenario within a large university library. The library is responsible for digitizing and making accessible decades of scholarly articles and theses, in addition to ensuring all new digital acquisitions and faculty-created course materials meet accessibility standards. Manually tagging this immense volume of diverse documents would require a significant team of trained accessibility professionals and an extended timeline, leading to delays in content availability for students and researchers with disabilities. The cost associated with this manual effort would be substantial.
Implementing ADAPT AI fundamentally alters this scenario. The library can process large batches of documents through the ADAPT AI platform. The AI rapidly analyzes each PDF, automatically applying tags for headings, paragraphs, lists, and tables, and establishing the correct reading order. For images, the AI can identify their presence, prompting the accessibility team to provide or refine the alternative text. The accessibility professionals then review the AI-generated tags using ADAPT AI's interface, which highlights potential areas of concern or complexity. Using I PDF Accessibility QA tools, they can quickly validate the majority of the automated tags and focus their expertise on correcting any errors or refining the tagging of particularly challenging elements, such as complex data tables or intricate diagrams.
This streamlined workflow, powered by ADAPT AI's automation, allows the university library to process a significantly higher volume of documents in the same amount of time compared to a purely manual approach. The reported 70% reduction in manual tagging effort means that the accessibility team can dedicate more time to quality control, training, and addressing edge cases, ultimately leading to a higher standard of accessibility across their digital collection. The time and cost savings are substantial, enabling the library to meet compliance deadlines more effectively and provide equitable access to information for its entire community much sooner.
In the publishing industry, similar efficiencies are realized in producing accessible versions of journals, books, and other publications. Integrating ADAPT AI into the production workflow allows publishers to generate accessible PDF versions concurrently with or soon after the initial publication, rather than undertaking a separate, time-consuming manual remediation process. This accelerates time to market for accessible content and ensures that publications are born accessible to the greatest extent possible.
The impact of reducing manual PDF tagging effort by 70% with ADAPT AI extends beyond mere efficiency gains. It democratizes access to information, ensuring that students, researchers, and readers with disabilities can engage with digital content seamlessly and independently. It empowers educational institutions and publishing houses to meet their legal and ethical obligations more effectively and sustainably. By automating the foundational aspects of PDF tagging, ADAPT AI allows human expertise to be focused where it is most valuable – on ensuring the highest quality of accessibility for even the most complex documents.
For universities, colleges, research institutions, and publishers striving to enhance digital inclusivity and ensure compliance in an era of ever-increasing digital content, the manual burden of PDF tagging is a significant obstacle. ADAPT AI offers a powerful, AI-driven solution that addresses this challenge head-on, delivering substantial reductions in manual effort and accelerating the path to comprehensive PDF accessibility.
