skillfed

docling-parse

Simple package to extract text with coordinates from programmatic PDFs

docling-parse v7.13.0 4.6M downloads/30d#2,275 on PyPI326
Permissive license MIT Active released

What it is and what it does

Docling Parse is a Python wrapper around a C++ PDF parser that extracts structured text, geometric coordinates, and images from programmatic PDFs. It splits parsing into two phases: a fixed `DecodeConfig` applied at document open time (controlling sanitization and glyph handling) and a per-page `ContentConfig` that determines what to compute and materialize (character cells, word cells, line cells, shapes, bitmaps). This separation allows cheap initial loading and selective enrichment on demand—if you request richer output later, the page is re-decoded automatically.

The package supports both sequential parsing (one PDF at a time) and parallel multi-threaded parsing with backpressure control. It includes a CLI for single-file processing and integrates with the broader Docling PDF conversion ecosystem. The library is actively maintained, supports Python 3.10–3.14 across major platforms, and provides performance benchmarks against other PDF packages.

Use it for:

  • Extract word-level bounding boxes and text from PDFs for document layout analysis or OCR validation.
  • Batch-process multiple PDFs in parallel with configurable thread pools and result backpressure.
  • Render pages as images with overlaid cell boundaries (character, word, or line level) for debugging or visualization.
  • Selectively materialize only the content levels needed per page to optimize memory and CPU in large-scale workflows.
  • Integrate PDF parsing into document conversion pipelines that require both text and spatial metadata.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Extracts text, coordinates, and bitmap images from programmatic PDFs with support for character, word, and line-level granularity, offering both sequential and multi-threaded parsing modes.

Yes. Docling Parse is actively maintained, permissively licensed, and offers a well-designed API for structured PDF extraction with multi-threaded support. Install friction is moderate due to compiled components, but pre-built wheels cover all major platforms and Python versions. Suitable for production document processing workflows.

Install

docling-parse on PyPI

pip

pip install docling-parse

uv

uv add docling-parse

poetry

poetry add docling-parse

Installing docling-parse

Before you install

Medium install friction due to compiled C++ components with pre-built wheels for Python 3.10–3.14 across macOS, Linux, and Windows. Active maintenance with a release on 2026-08-14 and 326 repository stars.

License in practice

MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects.

Quickstart

pip install docling-parse

from docling_parse.pdf_parser import DoclingPdfParser, DecodeConfig, ContentConfig, ContentLevel

parser = DoclingPdfParser(loglevel="fatal")
pdf_doc = parser.load(
    path_or_stream="file.pdf",
    decode_config=DecodeConfig(do_sanitization=True),
    content_config=ContentConfig(
        word_cells_content_level=ContentLevel.COMPUTE_AND_MATERIALIZE,
    ),
)

for page_no, page in pdf_doc.iterate_pages():
    for word in page.iterate_cells():
        print(word.rect, word.text)

Requires Python >=3.10; compiled wheels depend on system C++ runtime libraries.

Verify before relying

  • Whether the package handles encrypted or password-protected PDFs beyond what the CLI suggests.
  • Performance characteristics on very large PDFs or batch workloads compared to alternatives.
  • Memory footprint when materializing all cell levels for high-page-count documents.

Package facts

License MIT (permissive)
Python support supports the current Python release (>=3.10)
Install friction medium — platform-specific wheel
Runtime dependencies 4 — pillow, pydantic, docling-core, pywin32
Maintenance actively maintained — 0 days since the last release
Last repo commit
First released
Downloads 4,605,553/month — #2,275 on PyPI (30-day window, as of 2026-08-14)
Known vulnerabilities none known (OSV.dev, checked 2026-08-14)

Evidence: docling_parse-7.13.0-cp310-cp310-macosx_14_0_arm64.whl; docling_parse-7.13.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; docling_parse-7.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; docling_parse-7.13.0-cp310-cp310-win_amd64.whl; docling_parse-7.13.0-cp310-cp310-win_arm64.whl; docling_parse-7.13.0-cp311-cp311-macosx_14_0_arm64.whl; docling_parse-7.13.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; docling_parse-7.13.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; docling_parse-7.13.0-cp311-cp311-win_amd64.whl; docling_parse-7.13.0-cp311-cp311-win_arm64.whl; docling_parse-7.13.0-cp312-cp312-macosx_14_0_arm64.whl; docling_parse-7.13.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; docling_parse-7.13.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; docling_parse-7.13.0-cp312-cp312-win_amd64.whl; docling_parse-7.13.0-cp312-cp312-win_arm64.whl; docling_parse-7.13.0-cp313-cp313-macosx_14_0_arm64.whl; docling_parse-7.13.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; docling_parse-7.13.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; docling_parse-7.13.0-cp313-cp313-win_amd64.whl; docling_parse-7.13.0-cp313-cp313-win_arm64.whl

Keywords: docling, pdf, parser

Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: C++Programming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14

Tags

pdf text extraction with coordinatespdf parsing pythonextract text from pdfpdf document parserprogrammatic pdf processingpdf image extractionbatch pdf parsing
pdf-parsingdocument-extractionmulti-threaded

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