What Is the Best PDF Remediation Tool?

If you work in higher education, government, or any organization subject to accessibility regulations, you have probably already discovered the hard part: finding accessibility problems is not the same as fixing them. Scanners are plentiful. Tools that actually remediate — that modify the PDF structure, add missing tags, generate alt text, and produce a validated output — are far fewer.

So what should you look for in a remediation tool, and how do the available options compare?

What "Remediation" Actually Means

Before evaluating tools, it helps to define the term precisely. A PDF remediation tool should modify the document itself to resolve accessibility failures. That means writing correct structure tags into the PDF's tag tree, adding or correcting alt text on images, fixing reading order, repairing table markup, and embedding OCR text layers where needed.

If a tool only generates a report listing problems, it is a scanner, not a remediator. Both are useful. But they solve different problems, and confusing the two leads to wasted budget and missed deadlines.

Key Evaluation Criteria

When comparing remediation tools, these are the questions that matter most:

Does it actually modify the PDF? Some tools export to a new format or produce an HTML alternative. That can be a valid accessibility strategy, but it is not PDF remediation. If you need the output to be a tagged, accessible PDF, confirm the tool writes back to PDF.

Does it handle OCR? Scanned documents — image-only PDFs — are common in university archives. A remediation tool that cannot extract text via OCR will skip these entirely, leaving a major gap in your compliance posture.

How does it handle structure tagging? Headings, lists, paragraphs, and sections all need correct tags in the PDF's logical structure tree. Ask whether the tool infers heading levels from visual formatting, or whether it just applies a flat structure.

Can it generate alt text? Image descriptions are one of the most time-consuming parts of manual remediation. AI-generated alt text has improved dramatically, but quality varies. Look for tools that provide confidence scoring so you know which descriptions are reliable and which need human review.

Does it repair table markup? Tables are notoriously difficult in PDF accessibility. Correct remediation means tagging header cells, associating data cells with headers, and handling merged cells. Many tools punt on this entirely.

Does it fix reading order? A PDF can have correct tags but present them in the wrong sequence. Multi-column layouts, sidebars, and footnotes all create reading order challenges that the tool should address.

Does it validate the output? Remediation without validation is guesswork. The tool should check its own work against a recognized standard — ideally both the Matterhorn Protocol (the definitive PDF/UA test suite) and structural validators like veraPDF.

Can you audit the logic? When a regulator or a student asks why a particular remediation decision was made, can you explain it? Tools that operate as black boxes make compliance auditing difficult.

Does it support self-hosting? For institutions handling student records, research data, or FERPA-protected information, sending documents to a third-party cloud service may not be acceptable. Self-hosting gives you control over where data lives.

Category 1: Manual Tools

Adobe Acrobat Pro is the most established option. Its accessibility tools — the Reading Order panel, tag editor, and built-in checker — are powerful and flexible. An experienced operator can fix virtually any PDF accessibility issue.

The trade-off is speed and expertise. Manual remediation in Acrobat typically takes 30 minutes to several hours per document, depending on complexity. It requires significant training to use correctly. For a department with 50 PDFs, this is manageable. For one with 5,000, it is not.

Other manual tools like axesPDF and CommonLook PDF offer more guided workflows than Acrobat and are popular with professional remediators. They are excellent for complex documents that need human judgment, but they share the same fundamental scaling limitation.

Manual tools are the right choice when you have a small number of high-stakes documents and trained staff to work on them.

Category 2: Outsourced Services

Several companies offer remediation as a managed service. You upload documents, their team remediates them, and you receive accessible PDFs back.

Pricing typically ranges from $50 to $150 per document, depending on complexity and turnaround time. Quality varies significantly between providers — some employ experienced accessibility specialists, others rely on undertrained contract workers.

The advantages are clear: no training required, no software to maintain. The disadvantages are equally clear: cost scales linearly with volume, turnaround times can stretch to weeks, and you are sending potentially sensitive documents to a third party.

For institutions with strict data sovereignty requirements, outsourcing may not be an option at all. For those with large backlogs, the cost can be prohibitive — 5,000 documents at $100 each is half a million dollars.

Outsourced services work well for organizations with moderate document volumes, flexible timelines, and budgets that can absorb per-document costs.

Category 3: Automated Platforms

AI-powered remediation platforms represent the newest category. These tools use machine learning to analyze document structure, generate alt text, infer reading order, and apply fixes automatically — then validate the result.

The primary advantage is scale. An automated platform can process hundreds or thousands of documents in the time it takes a human to remediate one. The cost per document drops dramatically.

The trade-off is accuracy. AI remediation is not perfect, and any honest platform will acknowledge this. The key differentiator between automated tools is how they handle uncertainty. Does the system tell you when it is confident in a fix and when it is not? Can you set a confidence threshold below which fixes are flagged for human review instead of applied automatically?

Other important distinctions within this category:

• Model flexibility. Some platforms are locked to a single AI provider. Others let you choose or swap models — useful as the technology improves rapidly and no single model is best at every task.
• Batch processing. Can you point the tool at a folder, a learning management system, or a cloud drive and process everything, or do you have to upload files one at a time?
• Open source vs. proprietary. Proprietary platforms may deliver polished experiences, but you cannot inspect the remediation logic, self-host for data sovereignty, or adapt the tool to your specific needs. Open-core platforms give you that transparency.

Making the Choice

There is no single "best" tool — the right choice depends on your volume, budget, data sensitivity requirements, and staff expertise.

For small volumes with high complexity (legal documents, research papers with complex figures), manual tools or outsourced services often produce the best results.

For large volumes where speed and cost matter (course materials, administrative documents, archived content), automated platforms are the practical choice.

For institutions with strict data governance, self-hosted solutions eliminate the need to send documents to external services.

Many organizations will use a combination: automated processing for the bulk of their documents, with manual review reserved for the cases where AI confidence is low or the stakes are highest.

Where Aelira Fits

Aelira is open core, confidence-scored, and validates with both Matterhorn and veraPDF. The pipeline handles OCR, structure tagging, alt text generation, table repair, and reading order — and tells you exactly how confident it is in each fix. You can swap AI models, self-host the entire stack, and audit every remediation decision.

It is not the right tool for every situation. A single complex legal document with unusual formatting may still benefit from a skilled human in Acrobat Pro. But for the thousands of course materials, syllabi, and administrative PDFs that universities need to make accessible before the April 2027 deadline, automated remediation with transparent confidence scoring is the most practical path forward.

See the pipeline or try it free.