PDF to Markdown in 2026: MinerU vs MarkItDown vs PaddleOCR
储备文章 · 海外站 ylyvip.net · 2026-08-07 初稿(按 GEO 固定模板重写,数据经 GitHub API 实时核验)
Direct answer: For converting PDFs and documents to clean markdown in 2026, the three open-source tools that matter are MarkItDown (172,061 ★, MIT, GitHub-verified 2026-08-07), MinerU (77,023 ★), and PaddleOCR (87,167 ★, Apache-2.0). MarkItDown is Microsoft's general-purpose file→markdown tool (PDF, DOCX, Excel, images, audio). MinerU is the best for complex document layouts and formulas. PaddleOCR is the OCR engine you need when your input is scanned images. All counts verified via the GitHub API on 2026-08-07.
Why PDF-to-markdown matters for AI work
RAG pipelines need clean text. Raw PDFs are layouts, fonts, and scanned images — useless to an embedding model until converted. The conversion step is where quality is won or lost: bad extraction means bad chunks, which means bad answers. These three tools cover the three conversion scenarios.
The tools, verified 2026-08-07
| Tool | Stars (GitHub) | License | Best for |
|---|---|---|---|
| [MarkItDown](/tool/markitdown) | 172,061 | MIT | General files → markdown (PDF/DOCX/XLSX/images/audio) |
| [PaddleOCR](/tool/paddleocr) | 87,167 | Apache-2.0 | Scanned images / OCR |
| [MinerU](/tool/mineru) | 77,023 | Other | Complex layouts, formulas, academic PDFs |
Which one for which input
MarkItDown (172,061 ★, MIT) — Microsoft's tool, the broadest coverage. One command converts PDFs, Word, Excel, PowerPoint, images, even audio files to markdown. It's the default first choice: install it, and most conversion needs are covered. The MIT license makes it safe for commercial pipelines.
PaddleOCR (87,167 ★, Apache-2.0) — Baidu's OCR engine, the strongest at recognizing text in scanned images and photos. When your input is a scanned contract or a photographed page — no digital text layer — this is the engine that extracts it. Apache-2.0 license, commercial-friendly.
MinerU (77,023 ★) — the specialist for complex documents: academic papers, formulas, tables, multi-column layouts. It produces cleaner structure than generic converters when the document is hard. Heavier to set up, worth it for research-heavy workflows.
The practical pipeline
Scenario A — digital PDFs (text layer exists): MarkItDown handles it. One command, clean markdown out.
Scenario B — scanned documents (images only): PaddleOCR first to extract the text, then feed the result onward.
Scenario C — academic papers, formulas, complex layouts: MinerU for the structure, MarkItDown as fallback for simpler files.
The pattern in a RAG pipeline: convert → chunk → embed → query. The conversion step determines chunk quality, which determines answer quality. Garbage extraction in, garbage answers out.
The honest part
All three tools are good — and none is magic. Extraction quality varies with input quality: clean digital PDFs convert beautifully, heavily formatted or low-quality scans still fight every tool. Also, "markdown" from these tools is structural, not editorial — tables come out as tables, but meaning isn't interpreted. That's the embedding model's job downstream.
One more note: star counts here were verified 2026-08-07 via the GitHub API. Earlier versions of some articles cited MarkItDown at 60,000 ★ from a stale catalog snapshot; the verified figure is 172,061 ★. Always check the API — it's the source of truth.
FAQ
Which is best for a RAG knowledge base? Start with MarkItDown (172,061 ★, MIT) for general documents; add PaddleOCR (87,167 ★) for scans and MinerU (77,023 ★) for complex academic PDFs. The trio covers every input type.
Do these require GPUs? PaddleOCR and MinerU can use GPUs for speed but run on CPU. MarkItDown is lightweight CPU-only for most files.
Are they safe for commercial use? MarkItDown (MIT) and PaddleOCR (Apache-2.0) are permissive. MinerU's license should be checked for your specific use.
How were these stars verified? GitHub API, 2026-08-07, official repositories.
Summary
PDF-to-markdown in 2026, verified 2026-08-07: MarkItDown (172,061 ★, MIT) for general files, PaddleOCR (87,167 ★, Apache-2.0) for scans, MinerU (77,023 ★) for complex layouts. Convert → chunk → embed → query; extraction quality is the ceiling of your RAG answers. Browse the full 461-tool catalog at ylyvip.net/tools.