pdf2docx-plus 0.6.3

Hardened PDF->DOCX converter. Fork of pdf2docx with stability fixes, typed API, plugin architecture, and optional ML layout/OCR/table backends.

pdf2docx-plus

Hardened fork of pdf2docx — a Python PDF → DOCX converter that actually writes editable Word documents (not Markdown, not HTML).

What's different from upstream

	upstream pdf2docx	pdf2docx-plus
Python support	3.10+	3.11 / 3.12 / 3.13
Hyperlink OOXML	nested inside <w:r> (invalid)	paragraph-level <w:hyperlink> (valid)
NULL-byte / control chars	sometimes leaks into <w:t>, corrupts DOCX	stripped at run insertion
Errors	single ConversionException	InputError / ParseError / MakeDocxError / PasswordRequired / TimeoutExceeded
Typed API	no	py.typed, dataclasses, Protocol-based plugins
Return value	None	ConversionResult with per-page accounting
Timeout	none (can hang forever)	timeout_s= watchdog
Plugin architecture	no	swap table / layout / OCR / formula backends
REST server	no	pdf2docx-plus serve (FastAPI, optional)
ML hooks (opt-in)	no	Table Transformer, Granite-Docling, PaddleOCR, pix2tex
Tables → CSV	no	--tables-csv DIR
Structured logging	hijacks root logger	scoped pdf2docx_plus logger

Install

pip install pdf2docx-plus            # core
pip install 'pdf2docx-plus[rest]'    # + FastAPI server
pip install 'pdf2docx-plus[bench]'   # + evaluation harness
pip install 'pdf2docx-plus[ml-tables]' # + Table Transformer (torch)
pip install 'pdf2docx-plus[ml-ocr]'  # + PaddleOCR

Quick start

from pdf2docx_plus import convert

result = convert("in.pdf", "out.docx", timeout_s=120)
print(result.pages_ok, "/", result.pages_total, "pages in", result.elapsed_s, "s")

Or with more control:

from pdf2docx_plus import Converter, PluginRegistry
from pdf2docx_plus.hooks import TableTransformerDetector

plugins = PluginRegistry()
plugins.add_table_detector(TableTransformerDetector(device="cuda"))

with Converter("in.pdf", password="s3cret") as cv:
    result = cv.convert(
        "out.docx",
        pages=[0, 1, 2],
        profile="fidelity",     # "fast" | "fidelity" | "semantic"
        timeout_s=60,
        continue_on_error=True,
    )
    for p in result.page_results:
        if not p.ok:
            print(f"page {p.page_index}: {p.error}")

CLI

pdf2docx-plus convert in.pdf out.docx --timeout 120 --profile fidelity
pdf2docx-plus convert in.pdf --pages 0,2,5 --tables-csv tables/
pdf2docx-plus extract-tables in.pdf --out tables.json
pdf2docx-plus serve --host 0.0.0.0 --port 8000
pdf2docx-plus version

REST server

pip install 'pdf2docx-plus[rest]'
pdf2docx-plus serve --port 8000
# in another shell:
curl -F file=@in.pdf -F profile=fidelity http://localhost:8000/convert -o out.docx

Endpoints:

Method	Path	Body	Returns
POST	/convert	multipart file, optional password, profile, timeout_s	DOCX bytes + X-Pages-Ok / X-Pages-Failed / X-Elapsed-Seconds headers
POST	/extract-tables	multipart file, optional password	JSON {"tables": [...]}
GET	/healthz	—	{"status": "ok"}
GET	/version	—	{"version": "..."}

Plugin architecture

Four extension points, all Protocol-based:

from pdf2docx_plus.plugins import (
    TableDetector, LayoutDetector, OcrEngine, FormulaRecognizer
)

Register any implementation on PluginRegistry and pass it to Converter. Plugins never kill a conversion — exceptions raised inside a plugin are logged and skipped.

Built-in ML hooks (opt-in extras):

Hook	Backend	Extra	Weights license
TableTransformerDetector	HuggingFace microsoft/table-transformer-*	ml-tables	MIT
GraniteDoclingLayoutDetector	ibm-granite/granite-docling-258M	ml-layout	Apache-2.0
PaddleOcrEngine	PaddleOCR	ml-ocr	Apache-2.0
Pix2TexFormulaRecognizer	pix2tex	ml-formula	MIT
UniMERNetFormulaRecognizer	UniMERNet (bring weights)	manual	Apache-2.0

Benchmark

pip install 'pdf2docx-plus[bench]'
python -m bench.run --corpus bench/corpus --out bench/reports/latest.json

Metrics implemented: text F1, TEDS (apted), reading-order Kendall-tau, rendered SSIM (via LibreOffice + scikit-image), and editability ratio.

Seed corpus in this repo: 3 financial fund PDFs (born-digital). Drop more under bench/corpus/<name>/input.pdf and, optionally, expected_text.txt, expected_tables.json, expected_order.json for scoring.

Current baseline on the seed corpus (76 pages, CPU):

awhkef                  9 pages   0 failed    7.1 s   74 KB
first_sentier          58 pages   0 failed   15.8 s  155 KB
kfs_bosera              9 pages   0 failed    4.3 s   87 KB
TOTAL                  76 pages   0 failed   27.7 s  2.75 pg/s

Licensing

pdf2docx-plus is MIT, but depends on PyMuPDF (AGPL-3.0) — this propagates to you if you redistribute or expose as a network service. See LICENSING.md for the full dependency matrix, AGPL implications, and the future pypdfium2 migration path.

What's NOT done yet (roadmap)

This fork covers Phase 0 (foundation) and most of Phase 1 (stability

• typed API) from the original 21-week PDF2DOCX_FORK_PLAN.md. Phases 2–5 are scaffolded via the plugin architecture but the ML-backed hooks need real integration work to reach the v1.0 success criteria in the plan (TEDS ≥ 0.90, text F1 ≥ 0.98, reading-order Kendall-tau ≥ 0.90).

Specifically, still open:

• Train / evaluate Table Transformer + Granite-Docling against an annotated corpus (plan §K).
• Cross-page table stitching heuristic (§B.7).
• Header/footer → w:hdr / w:ftr emission (§C.13).
• Math recognition pipeline wiring (§F.24).
• Scanned-PDF OCR routing + auto-detect (§G.25).
• styles.xml rewrite (§H.27) — currently we still use python-docx defaults.
• pypdfium2 backend for permissive licensing (§6).

Credits

Forked from ArtifexSoftware/pdf2docx (originally by @dothinking). MIT.