unPDF
Quickly extract text characters and character metadata from pdfs using pdfium.
Decision gist · record as of 2026-08-14
Yes, if you need character-level PDF metadata and positioning. The zero-dependency design and precompiled wheels make installation straightforward. However, the aging maintenance status (400 days since release) and lack of visible repository activity suggest limited ongoing support—verify stability for production use before committing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.9; PyArrow optional but recommended for data analysis workflows.
- Medium install friction due to compiled wheels for multiple Python versions and platforms (cp310–cp313, macOS/Linux/Windows).
- Aging maintenance status (400 days since release) suggests limited ongoing support.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; attribution required.
last release 2025-07-10 (400 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 101,999 downloads/mo, #12,899 on PyPI
Alternatives
Verify before relying
pip install unpdf
from unpdf import extract
pages, chars, text_objs, fonts = extract("document.pdf")
text = ''.join(chr(c) for c in chars.arrays['char'])
print(text)- Whether PDFium is bundled in wheels or requires separate system installation.
- Performance characteristics on large PDFs or high-volume extraction tasks.
- Stability and bug-fix frequency given aging maintenance status.
What it is and what it does
unPDF is a Python library that extracts text and metadata from PDF files at the character level using PDFium. It returns four table objects—pages, characters, text objects, and fonts—each containing structured data: character positions and bounding boxes, font sizes and names, RGBA color values, and transformation matrices. The library ships with no runtime dependencies and optionally integrates with PyArrow for conversion to pandas or polars DataFrames.
The package is designed for developers who need precise, granular access to PDF content rather than simple text extraction. It exposes low-level PDFium data structures directly, making it suitable for document analysis, layout reconstruction, and metadata-driven workflows. Installation requires Python >=3.9 and uses precompiled wheels for common platforms.
Use it for
- Extract character positions and bounding boxes for document layout analysis or OCR validation.
- Retrieve font metadata and color information to reconstruct visual styling or detect formatting changes.
- Convert PDF character data to pandas/polars DataFrames for statistical analysis or data science pipelines.
- Build custom text reconstruction logic that preserves spatial relationships and transformation matrices.
- Analyze multi-language PDFs by accessing unicode character codes and mapping errors.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need character-level PDF metadata and positioning.
The zero-dependency design and precompiled wheels make installation straightforward. However, the aging maintenance status (400 days since release) and lack of visible repository activity suggest limited ongoing support—verify stability for production use before committing.
Install
unpdf on PyPI
Before you install
Medium install friction due to compiled wheels for multiple Python versions and platforms (cp310–cp313, macOS/Linux/Windows). Aging maintenance status (400 days since release) suggests limited ongoing support.
Requires Python >=3.9; PyArrow optional but recommended for data analysis workflows.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; attribution required.
Quickstart
pip install unpdf
from unpdf import extract
pages, chars, text_objs, fonts = extract("document.pdf")
text = ''.join(chr(c) for c in chars.arrays['char'])
print(text)Verify before relying
- Whether PDFium is bundled in wheels or requires separate system installation.
- Performance characteristics on large PDFs or high-volume extraction tasks.
- Stability and bug-fix frequency given aging maintenance status.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Aging 400 days since the last release |
| First released | |
| Downloads | 101,999 / month, #12,899 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: unpdf-1.0.0-cp310-cp310-macosx_11_0_arm64.whl; unpdf-1.0.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; unpdf-1.0.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; unpdf-1.0.0-cp310-cp310-win_amd64.whl; unpdf-1.0.0-cp310-cp310-win_arm64.whl; unpdf-1.0.0-cp311-cp311-macosx_11_0_arm64.whl; unpdf-1.0.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; unpdf-1.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; unpdf-1.0.0-cp311-cp311-musllinux_1_2_aarch64.whl; unpdf-1.0.0-cp311-cp311-musllinux_1_2_x86_64.whl; unpdf-1.0.0-cp311-cp311-win_amd64.whl; unpdf-1.0.0-cp311-cp311-win_arm64.whl; unpdf-1.0.0-cp312-cp312-macosx_11_0_arm64.whl; unpdf-1.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; unpdf-1.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; unpdf-1.0.0-cp312-cp312-musllinux_1_2_aarch64.whl; unpdf-1.0.0-cp312-cp312-musllinux_1_2_x86_64.whl; unpdf-1.0.0-cp312-cp312-win_amd64.whl; unpdf-1.0.0-cp312-cp312-win_arm64.whl; unpdf-1.0.0-cp313-cp313-macosx_11_0_arm64.whl
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See also pdfplumber · pdftext · pymupdf · pdfminer · pypdfium2 · pdfminer.six · fillpdf · pymupdf-fonts · pdf-oxide · playa-pdf