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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesI rebuilt an “Export text for AI” feature three times in one day because I had not decided what “body text” meant—or which information belonged in the file an AI would read. The final design separates extracted PDF text from the findings a person uses to check the PDF. That distinction matters: a checker’s “safe” verdict can become part of the material an AI summarizes.
The problem was deciding what the AI should receive
At Okinawa Software Lab, I was building a feature to export PDF text for an AI summary. The first question seemed straightforward: extract the text, then give it to the model. But the export also contained scan results, and I had not separated the document’s content from the tool’s judgments about it.
That made the file serve two different readers. The AI needed a representation of the PDF’s text; a human reviewer needed the checker’s findings and a way to verify them. Combining those jobs created the first mistake.
Version 1: scan results mixed with body text
The first export included both extracted text and check results. A finding such as “Dangerous mechanisms: none” was intended to inform a human reviewer. In the combined file, however, it could also become context for the AI’s summary. A checker result is not part of the PDF’s body text, and placing it beside that text risks treating a tool’s judgment as document content.
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I also needed to handle content judged invisible by comparing rendered output with extracted text. Instead of copying that content into the AI-facing file, this version used a marker to indicate that content had been replaced. The marker preserves evidence that something was omitted without putting the omitted text back into the model’s input.
Version 2: cleaner notes, but still two audiences
The second version added wording to explain limitations and joined line wraps so the text would be easier to read. But it was still a long file combining notes and body text. The export continued to ask one document to serve both the AI and the human reviewer.
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More importantly, the extraction and visibility checks did not justify promising that every kind of hidden or mismatched text had been removed. Adding explanatory notes could not make the underlying method more complete. The design needed a narrower claim, not more confident wording.
Version 3: body text and minimal provenance
The final version removed scan results and explanatory notes from the AI-facing Markdown. It kept the filename and a short provenance-and-limitations line, while leaving the HTML report as the place for a person to review the findings.
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For this feature, the export meant pdfium-extracted characters, with spans judged invisible by render comparison replaced by markers. That defines what the file contains without claiming it is a complete or authoritative transcript of everything encoded in the PDF.
What this visibility check can—and cannot—catch
In the Okinawa Software Lab implementation described here, render comparison identifies spans judged invisible in the rendered page. Examples include text with the same color as the background, transparent or very small text, text outside the page, and text behind a shape. These are implementation-specific observations, not guarantees for PDF tools in general.
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The check does not undo /ActualText or font remapping. In those cases, replacement characters produced during extraction can remain in the body without a marker. It also does not perform OCR on images, guarantee reading order, or prove that no hidden content remains. A marker therefore describes a particular transformation; it is not a certificate that the remaining text is complete or safe.
Why extracted text is only one representation of a PDF
A PDF can contain more than a simple sequence of words. The PDF Association explains that AI systems vary in their handling of extraction, OCR, metadata, annotations, and Tagged PDF semantics. Tagged PDF can preserve logical reading order and structure in born-digital documents; scanned or image-based text may require OCR. Converting a PDF to plain text also loses some of the richer semantics the format can carry. PDF Association: AI and PDF
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This is why an AI text export should be understood as a chosen representation, not a complete copy of everything a PDF means. A plain-text or Markdown file may be sufficient when the task is summarization, but it can flatten the relationships among columns, tables, headings, and other layout features.
For layout-aware processing, output can retain additional evidence such as word coordinates or style information. pdfRest documents an API with options for coordinates, style, full-text modes, and line-break preservation, while warning that text order can vary on complex layouts. This is a vendor’s description of its API, not independent performance testing. pdfRest: PDF to Text API
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reading order and realistic test documents
In this implementation, text ordering follows pdfium’s drawing order. Columns and tables can therefore appear out of sequence. Joining line wraps can make extracted paragraphs easier to read, but it cannot reconstruct every layout relationship or guarantee the intended order.
One real Japanese PDF also exposed a full-width character spacing bug that synthetic English PDFs had not revealed. I traced it to a helper-name collision and fixed it. It was one incident, not a measure of overall performance, but it showed why realistic documents—and languages beyond the ones used in quick test files—belong in testing.
A practical boundary between AI input and human review
- Define the AI input: specify whether it contains extracted document text only, and name any transformations such as replacing spans with markers.
- Keep tool judgments separate: put scan results and verification details in a human-facing report rather than alongside text the AI is asked to summarize.
- Make omissions legible: use a marker to record that content was replaced, without copying that content into the AI-facing file.
- Describe the method narrowly: explain what the extraction and visibility check do, and avoid implying that they find every form of hidden or mismatched text.
- Test real layouts and languages: include documents with columns, tables, line wraps, scanned pages, and non-English text where those cases matter to the intended workflow.
- Leave a verification path: give people a separate report they can use to inspect findings against the PDF.
The three versions were not three different extraction algorithms. They were three attempts to draw the boundary between the text the AI should receive and the evidence a person needs to judge that text. Once that boundary was explicit, the export could make a narrower, more honest claim.
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