When working on PDF extraction tasks for large-scale document processing and knowledge workflows, especially involving llm, i needed an efficient alternative to Marker PDF. i’ve evaluated and tested multiple open-source libraries, checking for their speed, accuracy, layout retention, image extraction, and ease of integration with LLM pipelines. Below is my hands-on review of different tools i explored and how they stack up.
My Evaluation Criteria
i focus the following aspects:
- license – open source friendly for commercial projects.
- page limitations – whether there's a hard limit.
- image extraction – support for embedded or scanned images.
- output types – markdown, json and plain text .
- layout undarstanding – whether it preserves layout elements like tables, columns, and headers.
- ease of use – setup, community, and speed.
| sn | library name | license | image extraction | output types | layout details | pypi link | comments |
|---|---|---|---|---|---|---|---|
| 1 | PyMuPDF4LLM | agpl-3.0 | yes | Markdown, Llamaindex Docs | Limited | link | very fast and effective. minimal setup. |
| 2 | pdfplumber | mit | limited | JSON, Text | Yes | link | good for table heavy pdfs. |
| 3 | pdfminer.six | mit | no | Text | Yes | link | works but average accuracy. |
| 4 | markitdown | mit | no | Markdown | No | link | basic converter, nothing special. |
| 5 | nougat-ocr | apache 2.0 | yes | MultiMarkdown | No | link | good results, but painfully slow. |
| 6 | pdf-to-markdown | mit | yes | Markdown | No | - | decent, but loses some structured data. |
| 7 | olmocr | apache 2.0 | yes | Markdown | Yes | link | excellent results with structure retained. |
| 8 | docling | mit | yes | Markdown, JSON and HTML | Yes | Demo | tested via colab, very effective. |
| 9 | EasyOCR | apache 2.0 | images only | List, JSON | Yes (image) | link | only for ocr, doesn't retain layout. |
| 10 | OCRmyPDF | mpl 2.0 | yes | PDF/A | Yes | link | complicated setup, good ocr layer addition. |
| 11 | LayoutParser | apache 2.0 | no | JSON | Yes | link | use with other ocr tools for layout parsing. |
| 12 | pandoc | gpl 2.0 | no | Markdown | No | link | did not work well in pdf to md conversion. |
| 13 | pypdfDirectoryLoader | open source | no | Text, Markdown and HTML | No | - | works but lacks layout awareness. |
| 14 | pdfium | bsd-3 | no | CSV, Markdown, Text | No | - | good for low-level pdf rendering. |
| 15 | tableau | mit | no | CSV | No | - | only useful for table extraction. |
| 16 | pypdf | open source | not clear | Text | No | link | does not handle layout or images well. |
My top picks (as of now)
1. PyMuPDF4LLM
super fast and integrates well with llm pipelines like llamaindex. excellent markdown output. limited layout parsing, but still very usable
2. OlmoCR
surprisingly accurate with layout and structure.it even preserved indentation and titles nicely. great for fine-tuning document-based llms.
3. Docling
A great all-rounder, especailly with visual layout parsing.useful for html or json-based workflows.
4. pdfplumber
if you are dealing with tabular PDFs, this is your goto. Fast and battle-tested.
What did not Work for Me
- pandoc failed miserably with most PDF inputs for markdown conversion.
- MarkerDown and pdfminer.six struggled with layouts and formatting.
- OCR-only tools like EasyOCR and OCRmyPDF were limited to images or required post-processing.
Final thought
Marker PDF is solid, but not always the right fit, especially when you need control, open-source licensing, and llm-friendly formatting.tools like PyMuPDF4LLM, Olmocr, and Docling provide a powerful alternative with minimal trade-offs.
if you want something ready for fine-tuning datasets, these three would be my top choices to build on.
Please share your ideas in comment it will be very helpful for me to learn something
Top comments (1)
mineru-api is also one of the alternate but it provide only api service