PDF to Markdown Converter
PDF to Markdown: a PDF gives you words, not structure — copying a table means cleanup, and a scan has no text to copy at all. OhMyOCR reads digital and scanned PDFs into structured Markdown: real headings, GFM tables, LaTeX formulas, separate blocks instead of one flat dump. Every block stays linked to its spot on the page, so you can audit it before it reaches your docs, notes or LLM pipeline.
How it works
- 1
Upload the PDF — born-digital or scanned, a single page or a long report.
- 2
The parser rebuilds the file as separate blocks — headings, paragraphs, lists, tables, formulas — not one unbroken wall of text.
- 3
Check blocks against the rendered pages, drop headers and footers if you want, then export Markdown.
Why OhMyOCR
Structure-preserving Markdown
Headings come out as #-levels, tables as GFM pipe tables, display formulas as LaTeX — ready for a static site, a wiki, Obsidian or Notion without redoing the layout.
Built for LLM and RAG inputs
Retrieval pipelines choke on page furniture. At export you strip headers, footers and page numbers, so the Markdown chunks stay meaningful.
Auditable, block by block
Every parsed block points back to its exact region on the page. When a table or formula matters, one click verifies it — no trusting the dump.
Scanned PDFs included
Scans and photographed pages go through AI OCR first, then the same structured rebuild — no second workflow.
Why Markdown became the target format for documents
You rarely want a PDF for its own sake. What you want is its content somewhere you can actually use — a wiki, a static site, Obsidian, a git repo — and Markdown is the format those places consume. It is diffable in git, readable as plain text, and it sheds the layout baggage that makes PDF content so hard to reuse.
The catch is the converter. Naive ones dump a flat wall of text — tables mangled, headings gone. OhMyOCR parses the document into typed blocks first, then maps them: headings to #-levels, tables to GFM pipe tables, displayed equations to LaTeX. The Markdown mirrors the document instead of flattening it.
Clean Markdown for LLM and RAG pipelines
Retrieval is only as good as its input. Leak a running header into every chunk and the embeddings blur; flatten a table into word soup and the model answers from garbage. Fixing the input fixes both — that is what structured conversion does.
OhMyOCR treats page furniture as separate blocks you can exclude at export, and keeps tables and formulas intact. Each block also keeps its source coordinates, so when a pipeline answer looks wrong you can trace the chunk back to the exact region of the page it came from. JSON export with coordinates sits alongside Markdown.
Frequently asked questions
How do I convert a PDF to Markdown?
Upload the PDF in the workspace, wait for parsing to finish, review the blocks, then choose Export → Markdown. Headings, tables, lists and formulas come through as structure, not flattened text.
Does it work on scanned PDFs?
Yes. Scanned pages are recognized with AI OCR first, then rebuilt into Markdown. Every page is also rendered in the workspace, so you can compare the output with the original side by side.
Can I remove headers, footers and page numbers?
Yes. Headers and footers are detected as separate blocks, and the export center lets you exclude them — the Markdown then holds only real content.
Is the output good enough for LLM / RAG pipelines?
It's built for that. Structured Markdown with tables and formulas intact, plus optional JSON export with block coordinates, gives retrieval pipelines far cleaner chunks than flat OCR text.
How are tables and math handled?
Tables export as GFM pipe tables, or Excel/CSV if you prefer. Displayed equations come out as LaTeX, with a live preview you can edit before export.
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Try it on your own file
Free to start, no credit card, results you can verify line by line.
Get started — free