Native fast PDF text/table/image extraction (PyO3 binding over the turbo-parsepdf Rust core).
Project description
turbo-parsepdf
Fast native PDF text / table / image extraction for Python — a pure-Rust core
(PyO3, stable-ABI wheels). Imports as turbo_parsepdf. Output as a dict, or
HTML / Markdown / JSON strings.
pip install turbo-parsepdf
Benchmark vs the Python PDF stack
Wall-clock to extract every page's text, best-of-N (Apple M-series, release).
Reproduce: python benches/competitive-py/bench.py (after python3 benches/gen-corpus.py).
| document | turbo-parsepdf | pypdf | PyMuPDF (MuPDF, C) | pdfminer.six |
|---|---|---|---|---|
| 100 pages | 6.2 ms | 237 ms · 38× | 389 ms · 62× | 1920 ms · 307× |
| 20 pages | 1.1 ms | 80 ms | 103 ms | 419 ms |
| 2 pages | 0.06 ms | 2.6 ms | 4.0 ms | 18 ms |
Even including the Python FFI + dict-marshaling overhead, turbo is 38–307× faster — and its text is byte-identical to PyMuPDF (100% word recall).
import turbo_parsepdf
data = open("doc.pdf", "rb").read()
doc = turbo_parsepdf.parse(data)
# {"version": "1.7", "pages": [{"width": ..., "height": ..., "needs_ocr": False,
# "lines": [{"text": ..., "x": ..., "y": ...}],
# "tables": [{"rows": ..., "cols": ..., "cells": [[...]]}],
# "images": [{"name": ..., "format": "Jpeg", "width": ..., ...}]}]}
turbo_parsepdf.parse_to_markdown(data) # str
turbo_parsepdf.parse_to_html(data) # str
turbo_parsepdf.parse_to_json(data) # str
# Encrypted PDFs: pass the user or owner password.
turbo_parsepdf.parse(open("locked.pdf", "rb").read(), password="secret")
A fatal parse fault raises ValueError with a stable code
(InvalidHeader, BadStream, …). Scanned/image-only pages come back with
needs_ocr=True (OCR is out of scope).
Supports cross-reference streams + object streams (PDF 1.5+), all standard
stream filters + predictors, /ToUnicode & encoding/AGL & CID font decoding,
ruled tables, image XObject extraction, and standard-handler decryption
(RC4 + AES-128/256, R2–R6).
Part of the turbo-parsepdf workspace. MIT.
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