Check a PDF for AI writing

Extract first, then detect

Detectors score language, not page layout. Pull the text with the PDF extractor — including OCR on scanned pages — then run the AI detector on that extract. Headers, footers and form fields that survive as noise can distort a short sample.

Native text PDFs are straightforward. Scans depend on OCR quality. If the extract is garbled, fix extraction before you trust any authorship signal.

What a PDF check can and cannot tell you

You get the same indicator scale as pasted text: sentence-level highlights and an overall reading. You do not get proof that ChatGPT wrote the file, and you do not get a forensic PDF provenance report from this workflow.

For résumés, essays and reports, pair the score with a human read of the flagged spans. Format-specific angles (CV, essay, cover letter) explain the false-positive shapes those documents invite.

File size, plans and privacy

Extraction limits follow your plan — including the 20 MB ceiling on eligible tiers. Treat uploads like any other document you would not want sitting in a training corpus: use the private workspace defaults and delete extracts you no longer need.

If you only need the words and a count before detecting, the standalone PDF-to-text and word-count utilities cover that step without opening the full detector UI.

Questions

Can I check a PDF for AI-written content?

Yes. Extract the text first (OCR if the pages are scanned), then run the AI detector on the extract. The score remains an indicator, not proof of authorship.

Do scanned PDFs work?

They work when OCR recovers readable text. Poor scans produce poor extracts, and poor extracts produce unreliable scores.

Is a PDF score more accurate than pasted text?

No. Once the text is clean, the engine is the same. The PDF path only changes how you obtain the words.

Continue on IA Checker

• Extract text from PDF
• PDF to text
• Essays
• AI Detector