Why Some PDF Checks Can Never Be Fully Automated
Automated testing tools are indispensable today for efficiently checking the accessibility of PDF documents. They detect numerous errors within seconds and help ensure compliance with the PDF/UA standard. However, even though modern technologies and artificial intelligence are becoming increasingly powerful, there is one crucial area that cannot be fully automated: the assessment of meaning, context, and comprehensibility. This article explains why this is the case and why human judgment will remain indispensable in the future.
What Can Be Checked Automatically
A large portion of the PDF/UA requirements is based on clearly defined technical rules. This is exactly where accessibility checkers like PAC really shine.
Among other things, the following can be checked automatically:
- Whether a PDF is correctly tagged
- Whether headings have been marked as such
- Whether images have alternative text
- Whether the document structure is complete
- Whether form fields are correctly marked
- Whether metadata and language have been defined
- Whether lists, tables, and other structural elements are technically correct
These tests provide clear results and can be reliably automated. They form the foundation of any efficient accessibility test.
Why Technology Alone Is Not Enough
However, accessibility means much more than simply complying with technical requirements. The goal is to ensure that content is understandable and usable for people with disabilities.
This is precisely where automated testing reaches its limits.
A tool can detect that alternative text is present. However, it cannot determine with absolute certainty whether that text meaningfully describes the content of an image.
While alternative text such as “diagram” technically meets the requirement, it provides very little information to blind users. Whether alternative text is actually helpful depends on the content of the image and the context within the document.
Meaning arises from context
Not all requirements for accessible PDF documents can be evaluated based on technical rules. Some checks require an understanding of the content and its context. For example, a tool can recognize that an element has been marked as a heading. However, it is not possible to automatically determine with absolute certainty whether the selected text is appropriate as a heading or is simply highlighted body text.
The same applies to other aspects of accessibility, such as:
- Is a heading meaningful and does it describe the section that follows?
- Does the link text convey its destination even without the surrounding text?
- Are form labels clearly understandable to users?
- Are table headers chosen in a way that makes the relationships between elements clear?
- Is the reading order in complex layouts actually logical?
These questions cannot be answered solely on the basis of technical rules. They require a human understanding of language, content, and context of use.
Why AI Cannot Fully Solve This Challenge
Why AI Cannot Fully Solve This Challenge: With modern AI techniques, it is now possible to analyze significantly more aspects than just a few years ago. For example, AI can recognize patterns, interpret content, or identify anomalies that traditional rule-based audits fail to detect.
That is why PAC also supports manual audits with AI-powered analyses.
Nevertheless, AI always works with probabilities. It can make suggestions, identify anomalies, or offer recommendations. However, whether a headline is appropriately chosen, an alternative text is sufficiently descriptive, or a table is structured in a way that is easy to understand remains a professional judgment call.
The strength of modern accessibility checkers does not lie in replacing people. Rather, they handle all checks that can be objectively and unambiguously automated. As a result, they save time, improve quality, and significantly reduce manual work.
PAC combines automation with intelligent support
PAC automates all objectively verifiable requirements of the PDF/UA standard while also clearly indicating which validation steps still require manual review.
With its new AI features, PAC also helps users analyze semantic structures, such as headings, paragraphs and tables, more quickly and identify potential issues early on. The final assessment remains transparent at all times and is always made by a human.
This combination of automated testing, AI support, and human expertise ensures high-quality testing without creating a false sense of security.
Conclusion
Full automation of PDF accessibility is not realistic, either today or in the future. This is not a technical shortcoming, but rather a consequence of the complexity of human language and communication.
Automated checks handle all tasks that can be clearly evaluated based on technical rules. Where meaning, comprehensibility, and context are required, human judgment remains indispensable.
That is precisely why PAC takes a balanced approach: maximum automation where possible, and intelligent support where humans play a crucial role. This approach enables efficient and reliable validation processes for accessible PDF documents.