pdftotext
Simple PDF text extraction
Decision gist · record as of 2026-08-14
Yes, if you can meet the system dependency requirement. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and solves a common problem with a clean API. The high install friction (C++ compilation) is a real barrier on some systems but not insurmountable—install the platform-specific Poppler headers first, then pip install. Worth the effort for production PDF text extraction.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires libpoppler-cpp development headers and C++ compiler installed on your system before pip install will succeed.
- High install friction: requires system-level compilation and platform-specific C++ libraries (libpoppler-cpp-dev on Debian/Ubuntu, poppler-cpp-devel on Fedora/RHEL, or poppler via Homebrew on macOS).
- Package is actively maintained as of 49 days ago.
License · maintenance · safety
MIT (permissive) — MIT license is permissive and imposes no restrictions on commercial or private use, modification, or redistribution.
last release 2026-06-26 (49 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 169,686 downloads/mo, #10,409 on PyPI
Alternatives
Verify before relying
pip install pdftotext
import pdftotext
with open("document.pdf", "rb") as f:
pdf = pdftotext.PDF(f)
for page in pdf:
print(page)- Whether password-protected PDF support works reliably across all PDF encryption standards.
- Performance characteristics when processing large PDFs or batch operations.
- Accuracy of text extraction from PDFs with complex layouts, images, or non-Latin scripts.
What it is and what it does
pdftotext is a Python wrapper around the Poppler C++ library that extracts text content from PDF files. It provides a simple interface: load a PDF file (optionally with a password), then iterate over pages or join all text into a single string. The package handles the low-level Poppler integration so you don't have to.
The main trade-off is installation complexity. Because it wraps a C++ library, you must have Poppler development headers and a C++ compiler on your system before installing via pip. Once those are in place, the API is straightforward—just open a file, create a PDF object, and read pages as strings.
Use it for
- Index PDF documents for full-text search by extracting all text and storing it in a search engine.
- Batch convert PDF content to plain text for processing or analysis pipelines.
- Read text from password-protected PDFs programmatically in automated workflows.
- Extract page-by-page text for document summarization or NLP tasks.
- Build a document ingestion layer that accepts PDFs and outputs structured text data.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you can meet the system dependency requirement.
The package is actively maintained, has no known vulnerabilities, uses a permissive license, and solves a common problem with a clean API. The high install friction (C++ compilation) is a real barrier on some systems but not insurmountable—install the platform-specific Poppler headers first, then pip install. Worth the effort for production PDF text extraction.
Install
pdftotext on PyPI
Before you install
High install friction: requires system-level compilation and platform-specific C++ libraries (libpoppler-cpp-dev on Debian/Ubuntu, poppler-cpp-devel on Fedora/RHEL, or poppler via Homebrew on macOS). Package is actively maintained as of 49 days ago.
Requires libpoppler-cpp development headers and C++ compiler installed on your system before pip install will succeed.
License in practice
MIT license is permissive and imposes no restrictions on commercial or private use, modification, or redistribution.
Quickstart
pip install pdftotext
import pdftotext
with open("document.pdf", "rb") as f:
pdf = pdftotext.PDF(f)
for page in pdf:
print(page)Verify before relying
- Whether password-protected PDF support works reliably across all PDF encryption standards.
- Performance characteristics when processing large PDFs or batch operations.
- Accuracy of text extraction from PDFs with complex layouts, images, or non-Latin scripts.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Actively maintained 49 days since the last release |
| First released | |
| Downloads | 169,686 / month, #10,409 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: pdftotext-4.0.0.tar.gz
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