August 12, 2026

You have a PDF and you need the LaTeX source back. Maybe it is a published paper, an old preprint, or a professor's scanned notes. There is no undo button for compilation, so every route is some form of reconstruction. The options differ enormously in how much of the document they actually recover and how much cleanup they leave you. Here is what works in 2026, ranked by how likely you are to end up with a document that compiles.
Underleaf's PDF to LaTeX converter is built for the whole-document case: multi-page papers with equations, tables, figures, and a bibliography. Upload the PDF and it returns LaTeX source you can compile immediately. That last part is the point. The output is compiled and previewed against the original, so a table that lost a column or an equation that broke doesn't slip through silently; the AI fixes compile errors before you ever see them.
It handles digitally-produced PDFs and legible scans, keeps document structure (sections, cross-references, figure placement), and exports a zip of source you can hand to a co-author. The free tier is enough to convert real pages before paying for anything. Where it isn't the answer: thousand-page scanned archives are a digitization project, not a self-serve upload.
Mathpix earned its reputation on equation OCR, and the recognition quality is genuinely strong. Its Snip workflow is quick for grabbing individual equations out of a paper: screenshot a formula, get the LaTeX. Full-document PDF conversion exists too, but the native output is Mathpix's own Markdown dialect, so a LaTeX document usually needs a conversion-and-cleanup pass afterwards, and page volume is metered on paid plans. If your real task is "this one equation", it is a fine tool. If your task is "this entire paper, compiling", you will spend the saved time on stitching.
When the document is short and mostly prose, converting just the math is often faster than converting everything. Image to LaTeX takes a screenshot of an equation or a table and returns the LaTeX for it. Free, no account, and the right size of tool when you are retyping the prose anyway. For handwritten sources there is a handwriting to LaTeX converter tuned for lecture notes and problem sets.
Free, scriptable, and instant: pull the text layer out of the PDF and mark it up yourself. For a text-only document like a letter, a syllabus, or prose chapters, this is honestly fine. The moment math enters, it collapses: text extraction flattens x^2 + \frac{1}{2} into x2 + 1 2, drops table structure entirely, and knows nothing about figures. Budget an hour per math-heavy page for the rebuild.
Pasting a page screenshot into a general chatbot produces plausible LaTeX, and for a paragraph or one display equation it is a reasonable trick. The problem at document scale is that nothing checks the output. General models drift on long inputs: a dropped table row here, an invented citation key there, subscripts that quietly swap. With no compile step and no side-by-side against the source, you become the verification layer. That is the job you were trying to automate.
| Option | Full documents | Math accuracy | Compile check | Price |
|---|---|---|---|---|
| Underleaf | Yes, structure kept | 98%+ on clean sources | Yes, automatic | Free tier |
| Mathpix | Via Markdown dialect | Strong | No | Metered |
| Screenshot converters | Equations only | Strong on snippets | No | Free |
| Text extraction | Text layer only | None | No | Free |
| General chatbots | Degrades with length | Unverified | No | Subscription |
Whichever route you take, keep the original PDF next to the output and spot-check the numbers. A conversion that is 99% right and 1% silently wrong is worse than one that fails loudly. Tools that compile and preview the result do this checking for you.
Yes. Underleaf converts PDFs to compilable LaTeX with a free tier, which is enough to test it on a real paper before deciding anything. Command-line text extraction is also free, but it only recovers plain text: every equation, table, and figure still has to be rebuilt by hand.
On digitally-produced PDFs, the best tools now sit above 98% on text and standard math. Accuracy drops with low-resolution scans, dense tables, and unusual notation, which is why a compiled preview matters: it turns silent mistakes into visible ones you can fix in seconds.
Scanned documents work if the scan is legible, since the conversion runs on vision models rather than the PDF's text layer. Handwritten math is harder but supported; see Underleaf's handwriting to LaTeX converter for that workflow specifically.
For one equation, that works. Over a full paper the failure mode is silent: a general chatbot will drop a row from a table on page 9 or invent a plausible-looking subscript, and nothing warns you. Purpose-built converters compile the output and check it against the source, so errors surface instead of shipping.
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