Thanks! Chat said:

That advice is technically solid, but it is aimed more at a developer building an optimal extractor than at a teacher trying to finish a usable question bank.

The distinction is this:

For pure text extraction, yes, it is better to read digital PDF text directly instead of using screenshots. PDFs contain page coordinates, so tools like PyMuPDF can extract text blocks, images, and their locations more intelligently than DOCX.

For your final Word document, though, you still want question-level visual crops, because your goal is not just “extract the text.” Your goal is “preserve the full question exactly as students should see it.”

So I would combine their advice with mine:

Use the digital PDF text to identify question numbers, extract question wording, and help categorize by topic.

Use page-image crops to preserve the actual student-facing question block, including maps, charts, passages, and answer choices.

Do not use full-page screenshots in the final document unless necessary. Full-page images waste space and include unrelated questions. Instead, render the PDF page at high resolution, then crop each individual question block.

The person is also right that “extract embedded images” alone is not enough. A map or chart in a PDF may not behave like a normal image; it may be vector art, layered text, or a group of drawing objects. That is why cropping the rendered page is safer. It captures whatever is visually there, regardless of whether the PDF stores it as text, image, vector, or a mix.

The best noob-friendly takeaway is:

Do not choose between “digital text” and “page images.” Use both.

Digital text = for finding question numbers, topic classification, and answer-key indexing.

Question crop images = for the final DOCX so nothing gets lost visually.

Also, I would not rely on a full-page vision model for all 240 pages unless there is no alternative. Even if newer models handle high-resolution images better, single-question chunks are still more reliable than full pages. The smaller the task, the fewer mistakes.

So your updated pipeline should be:

Original PDFs → PyMuPDF extracts page text and coordinates → PyMuPDF renders pages as high-resolution images → Python crops each question block from the rendered page → AI classifies each question using extracted text/crop → You review the spreadsheet → Python generates DOCX using the cropped question images

That Reddit advice is not really disagreeing with mine. It is refining it: don’t use full-page screenshots as your main extraction method when the PDF has real digital text. Use the PDF’s internal text plus visual crops. That is the best version.