A PDF is a container. Detectors score language, not page layout. The reliable workflow is always the same: recover clean text, then run the AI detector on that extract.
The short product page for this path is the PDF detector angle. This article walks the steps and the failure modes in more detail.
Extract first
Pull the words out of the file before you ask any authorship question. Headers, footers, form fields and repeated page furniture can survive as noise in a short sample and distort the reading.
On IA Checker, start with extract text from PDF or PDF to text. Copy the result, skim it, and only then open the detector. If you only need a count first, use PDF word count.
Native text vs scanned pages
Native text PDFs are straightforward: the extractor reads the embedded text layer. Scanned pages need OCR. Poor scans produce poor extracts, and poor extracts produce unreliable scores.
If the extract is garbled — broken words, columns mashed together, missing pages — fix extraction before you trust any AI reading. Do not escalate a student or candidate on OCR garbage.
Run the detector on the extract
Paste the recovered text into the AI detector, or continue from the upload flow if your plan supports document checks. Prefer the full section you care about, not a single abstract paragraph.
File-size and plan limits still apply on eligible tiers. Treat uploads like any other document you would not want sitting in a training corpus: use private defaults and delete extracts you no longer need.
Read the result
- Open sentence-level highlights, not only the headline percentage.
- Ask whether flagged spans are stock intros or the analytical middle.
- Compare to earlier writing from the same person when you can.
- For essays and CVs, use the format-specific angles — those genres false-positive easily.
For coursework, pair this with essays and AI detection in schools. For how to interpret the number, see how to read an AI detection score.
Limits that still apply
A PDF check does not make the score more certain. You still cannot prove ChatGPT authorship from a percentage. You still get false positives on formal, evenly paced prose and on many non-native English writers.
The PDF path only changes how you obtain the words. Once the text is clean, the engine and the discipline are the same as pasted prose — indicators for review, not proof for a file.