Two PDF files can look identical while requiring completely different conversion workflows. One contains real characters positioned on a page. The other contains a photograph of characters. A browser text extractor can read the first directly; the second needs optical character recognition (OCR).
Choosing the wrong workflow wastes time and can create deceptively empty or inaccurate Markdown. Fortunately, you can classify most documents in less than a minute without installing specialist software.
The selection test
Open the PDF in a modern browser or desktop viewer and drag across one sentence. In a text PDF, the viewer usually highlights individual characters or words. You should be able to copy the selection into a plain-text editor and see recognizable text.
In a scanned PDF, dragging may select the entire page as one image, draw a rectangular region without highlighting words, or do nothing. Copy and paste may produce no content. This is the clearest signal that the page lacks a text layer.
Do not stop after testing the cover. Many reports have an image cover followed by text pages, while archival bundles may combine born-digital documents and scans. Test a normal body page, a page near the middle, and any section that visibly differs in quality.
What copy and paste reveals
Selection alone is not enough because some scans include a hidden OCR layer beneath the image. The text can be selected even though what you see is still a photograph. Paste the sample into a plain-text editor and compare it character by character.
Look especially at names, dates, decimal values, punctuation, and characters with similar shapes. OCR commonly confuses 0 with O, 1 with l, and hyphens with em dashes. Poor alignment between the hidden layer and the visible image may cause the wrong words to be selected.
A hidden OCR layer can be usable, but it should be treated as already-recognized data rather than ground truth. Conversion will preserve its mistakes. If accuracy matters, proofread the Markdown against the visible scan or rerun OCR with a model suited to the document language.
Visual signs of a scan
Scanned pages often show uneven lighting, shadows near the binding, skewed baselines, dust, compression blocks, or slightly different paper colors. Characters may blur when zoomed because they are pixels. Born-digital text usually remains sharp at high zoom because the viewer redraws glyphs from fonts.
These are clues, not definitive rules. A PDF can contain a high-resolution screenshot of a perfectly clean digital page. Conversely, an old report may contain real text plus scanned signatures. The file can also be hybrid: searchable text over page images, or text pages mixed with scanned appendices.
The workflow should therefore be page-aware. A single classification for the entire file may hide exceptions that need manual treatment.
Document properties and command-line checks
PDF viewers often show whether fonts are embedded. A list of document fonts strongly suggests that at least some pages contain text, but it does not prove every page does. Metadata may identify the producing application, such as a word processor, scanner, or print driver.
Developers can use tools such as pdffonts and pdftotext from Poppler. If pdffonts file.pdf lists fonts and pdftotext file.pdf - prints coherent text, the file is likely suitable for text-layer conversion. An empty result suggests a scan; garbled output suggests an encoding problem.
Avoid uploading a confidential document merely to classify it. The selection test and local tools are enough for most cases. If organizational policy prohibits cloud processing, apply that rule before choosing an OCR provider.
When a text layer is still unusable
Some PDFs contain characters but map them incorrectly to Unicode. The viewer knows which glyph to draw, yet copy and paste produces symbols or unrelated letters. This often occurs with subset fonts, custom encodings, or older document generators.
Another problem is fragmented text. Every letter or word may be positioned separately with little logical ordering. Extraction succeeds technically but produces scrambled sentences. Multi-column pages can interleave lines from different columns even when every character is correct.
In these cases, a simple parser is not enough. A layout-aware model may reconstruct reading order, while OCR can sometimes replace a broken text layer by reading the visible page. Both approaches require careful validation because they infer structure that the source did not encode.
Choosing the next step
Use a browser PDF-to-Markdown converter when selected text pastes correctly, headings and paragraphs follow a mostly linear layout, and the document does not depend on complex tables or equations. It is fast, requires no account, and can keep the document on the device.
Use OCR when pages are images, the text layer is empty, or existing OCR is too inaccurate to trust. Choose a tool that supports the document language and preserves layout if columns or tables matter. For a small non-sensitive document, a cloud OCR service may be convenient. For confidential material, consider a reviewed local OCR installation.
Use a layout parser such as Marker or MinerU when the document has scientific notation, dense tables, complex columns, or formulas. These tools consume more compute but can model page structure beyond basic coordinate heuristics.
Validate before scaling
Whatever method you choose, convert a representative sample first. Check ordinary prose, the hardest page, and a page containing critical numbers. Record the error patterns and decide which are acceptable. A pipeline that produces readable paragraphs but corrupts account numbers is not acceptable for financial material; a pipeline that loses decorative page numbers may be completely fine.
After OCR, preserve the original scan alongside the Markdown until review is complete. The Markdown is a derived interpretation, not a replacement for source evidence. Add a note about the OCR tool and date if the output enters a long-lived archive or research dataset.
If the PDF passes the text-layer test but columns read incorrectly, continue with the multi-column PDF guide. If it contains tables, use the PDF table conversion checklist before publishing the result.
