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For a local workflow that converts both PDFs and DOCX files to Markdown while retaining tables, Docling is the clearest documented starting point. Its command-line interface supports both formats and Markdown output. For PDF-only work, PyMuPDF4LLM is another documented option that places detected tables in Markdown. Scanned PDFs need OCR, and either tool’s output should be checked against your own files.
Use Docling for a combined PDF and DOCX workflow
Docling’s v2 command-line documentation shows single-file PDF conversion with docling myfile.pdf, which produces Markdown by default. For batch conversion of PDF and Word files, it documents:
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docling ./input/dir --from pdf --from docx --to md --to json --output ./scratchThis processes files in the input directory and writes Markdown and JSON outputs to ./scratch. The JSON option is useful when you want a structured representation as well as a Markdown export. See the Docling v2 documentation for the CLI and document API.
Docling’s supported-formats guide describes PDF support in terms of layout, reading order and table understanding. For DOCX, it lists headings, lists and tables. Its document API exposes a structured DoclingDocument, supports reading-order iteration and provides export_to_markdown() when you need to work from Python rather than the CLI.
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Choose the right route for PDF-only conversion
If you only need PDFs, PyMuPDF4LLM offers a focused alternative. The PyMuPDF project README demonstrates pymupdf4llm.to_markdown("report.pdf"); the wrapper integrates extracted text and detected tables into Markdown alongside surrounding text. PyMuPDF also documents direct PDF table extraction and Markdown export. See the PyMuPDF README and page documentation.
That documentation does not establish an equivalent no-license DOCX conversion path for PyMuPDF4LLM. The README’s DOCX Markdown example imports pymupdf.pro and unlocks it with a license key, so it should not be treated as proof of a free, no-license DOCX workflow. For a single documented route covering both PDF and DOCX, Docling is the better-supported choice.
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Check whether the PDF needs OCR
A PDF with selectable text can usually provide text for extraction directly. An image-only scan contains page images instead, so text extraction needs OCR. PyMuPDF’s README notes that ordinary extraction from scanned pages returns no text without OCR; Docling’s format guide lists OCR as optional for PDFs and advises full-page OCR for scans. The guide lists DOCX without OCR.
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- Inspect OCR output for missed characters and reading-order errors, especially in dense pages or documents with unusual typefaces.
- For PDFs with troublesome character mappings, PyMuPDF’s README suggests OCR as a possible remedy.
Validate tables and reading order in your own files
Markdown can represent tables, but conversion still has to infer their rows and columns from page layout. Multi-column pages, merged cells, footnotes and scanned tables are worth checking closely. The official documentation describes supported capabilities; it does not establish perfect fidelity for every document.
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- Choose representative files: a text-based PDF, a scanned PDF if you have one, and a DOCX with the kinds of tables you need to preserve.
- Convert them with the intended route: Docling for the combined formats, or PyMuPDF4LLM for PDF-only Markdown.
- Compare the Markdown with the originals. Check headings, reading order, table row and column boundaries, cell contents, and OCR errors where applicable.
- If tables need more control, try PyMuPDF’s documented table-extraction functions and Markdown export for PDF pages, then compare that result with the integrated PyMuPDF4LLM output.
The reviewed project documentation provides no shared-corpus accuracy or speed benchmark between these tools. Which output is acceptable depends on your files and how much cleanup your workflow can tolerate. The Docling supported-formats page says it was checked against Docling v2.129.0 on 2026-09-22; repository and live documentation can change, so confirm command syntax and licensing against the current project material before building a repeatable pipeline.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What “without an LLM” means here
These documented workflows can produce Markdown without requiring an LLM call as a conversion step. That does not establish that every internal component is model-free. Docling’s technical report, dated 2025-01-27, describes a Python package and CLI using specialized models for layout analysis and table-structure recognition; it does not establish that the conversion path calls a large language model. If avoiding all machine-learning models is a requirement, rather than avoiding LLM calls, check the exact tool configuration and components you plan to use.
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