fibrum-pdf 1.1.0

A fast PDF extractor; a 500 pages/s alternative to Marker, Docling, PyMUPDF4LLM & others.

FibrumPDF

Extract 500+ pages a second on CPU.

Gives you tables, text, and their formatting, plus lower level information like bounding boxes and font sizes.

It outputs JSON for programmatic use, but still allows for Markdown.

Written for Python, Go at the core, with a touch of C to interface with MuPDF.

Full performance breakdown here

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Installation

pip install fibrum-pdf

There are wheels for Python 3.9–3.14 (inclusive of minor versions) on macOS (ARM/x64) and all modern Linux distributions.

To build from source, see BUILD.md.

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What it's good at

• Speed.
• Custom logic.
• No GPU needed.
• Iterating on parsing logic without waiting hours.

What it's bad at

• Scanned PDFs and images. It doesn't extract images, nor parse them.
• Complex layouts (think Forms, spreadsheet-style documents)

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Usage

Basic

from fibrum_pdf import to_json

result = to_json("example.pdf", output="example.json")
print(f"Extracted to: {result.path}")

You can omit the output field; it defaults to <file>.json

Collect all pages in memory

result = to_json("report.pdf", output="report.json")
pages = result.collect()

# Access pages as objects with markdown conversion
for page in pages:
    print(page.markdown)
    
# Access individual blocks
for block in pages[0]:
    print(f"Block type: {block.type}")
    print(f"Has {len(block.spans)} spans")

This still saves it to result.path; it just allows you to load it into memory. If you don't want to write to disk at all, consider providing a special path.

This is only for smaller PDFs. For larger ones, this may result in crashes due to loading everything into RAM. See below for a solution.

stream pages (memory-efficient)

result = to_json("large.pdf", output="large.json")

# Iterate one page at a time without loading everything
for page in result:
    for block in page:
        print(f"Block type: {block.type}")

Markdown

result = to_json("document.pdf", output="document.json")
pages = result.collect()

# Full document as markdown
full_markdown = pages.markdown

# Single page as markdown
page_markdown = pages[0].markdown

# Single block as markdown
block_markdown = pages[0][0].markdown

Command-line

python -m fibrum_pdf.main input.pdf [output_dir]

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Output structure

Each page is a JSON array of blocks. Every block has:

• type: block type (text, heading, paragraph, list, table, code)
• bbox: [x0, y0, x1, y1] bounding box coordinates
• font_size: font size in points (average for multi-span blocks)
• length: character count
• spans: array of styled text spans with style flags (bold, italic, mono-space, etc.)

Note that a span represents a logical group of styling. You'll find that most blocks only have one span.

Span fields

• text: span content
• font_size: size in points
• bold, italic, monospace, strikeout, superscript, subscript: boolean style flags
• link: boolean indicating if span contains a hyperlink
• uri: URI string if linked, otherwise false

See models.py.

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FAQ

why not XXX? There are tools that are much better in quality. These are typically reliant on some sort of ML or OCR, making them slow and GPU-dependent. There are also tools that are extremely fast, but only give you raw text; which isn't helpful. Hopefully, this is fast and good enough.

Will this handle my XXX PDF?   It won't handle scanned documents, images or weird layouts and elements (think Forms in PDFs and spreadsheet-like documents).

Commercial use?   This project uses MuPDF, which is under the AGPL-v3 license, or optionally a paid license from Artifex Software.

Motivations? I got bored waiting for my documents to get chunked again.

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Performance Breakdown

Using go/cmd/tomd/main.go with input_pdf [output_dir], I measured performance on:

• ~1600 page document (path not available)
• ~150 page document (test_data/pdfs/nist.pdf)

Performance depends on document size and available cores. With more pages to saturate your cores, you may see better throughput. Wall-clock time should scale approximately linearly with core count.

Test system: AMD Ryzen 7 4800H (8 cores, 6 used)

Runtime breakdown:

• Go code: ~25% of runtime
• MuPDF: ~75% of runtime

On the NIST document (150 pages): Go spent 78ms out of 363ms total (21%), MuPDF spent 285ms (79%).

Calculated average:

• 1600 pages in 3000ms + 150 pages in 350ms = 1750 pages in 3350ms
• ~520 pages/second

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Licensing

This project uses MuPDF, which is under the AGPL-v3 license, or optionally a paid license from Artifex Software.

See LICENSE for the detail.