paradox2

Fast, simple document extraction — a from-scratch rewrite of paradox-pdf, built around one idea: most PDFs are digital and don't need a GPU.

import paradox2 as pdx

pages = pdx.extract("invoice.pdf")

Why this rewrite exists

paradox-pdf grew into a 2,783-line facade doing routing, backend resolution, and overlay rendering all in one file. paradox2 starts over with a hard rule: digital pages never pay for OCR or vision models. Every PDF page is classified independently — a digital page (has a text layer) goes through a zero-ML fast path (PyMuPDF text + vector-line table detection); a scanned page routes to the GPU OCR pipeline. A single mixed document is handled correctly per-page, automatically.

Install

pip install paradox2

Everything above works with just that — digital PDFs, text, vector-line tables, key-value fields. Heavier features are opt-in extras so the base install stays small:

First-time setup (scanned/OCR support)

For scanned or photographed pages, run paradox2-setup right after installing the base package — it detects whether the machine has an NVIDIA GPU and installs the matching extras and the correct pinned paddlepaddle build for you:

pip install paradox2
paradox2-setup

This installs paradox2[gpu,rapidai,yolo,formats] plus paddlepaddle pinned to 3.2.1 (GPU build from PaddlePaddle's own index if a GPU is detected, plain CPU build otherwise). Pinning matters: paddlepaddle 3.3.1 — pip's unpinned default — has a real PIR/oneDNN inference bug; 3.2.1 is the verified-good version.

If you'd rather install extras manually instead of running the script:

pip install "paradox2[gpu,rapidai,formats]"

Use a clean virtual environment for [gpu]. Installing into a shared/base environment (e.g. Anaconda's base) that already has an older paddleocr or paddlepaddle from a prior project can leave an incompatible version in place — pip does not always resolve this cleanly against pre-existing packages in a polluted environment. python -m venv .venv && source .venv/bin/activate (or conda create -n paradox2 python=3.12) before installing avoids this.

What it does

import paradox2 as pdx
from dataclasses import asdict

# Every page, as a list of PageResult dataclasses (NOT dicts — a page
# doesn't support page["blocks"]; use page.blocks, or asdict(page) /
# the CLI's --format json for a plain-dict/JSON form) — digital pages
# via the fast path, scanned pages via GPU OCR, decided per page.
pages = pdx.extract("document.pdf")
page_dicts = [asdict(p) for p in pages]  # if you want plain dicts

# Just the text
text = pdx.extract_text("document.pdf")

# Just the tables (vector-line detection on digital pages,
# OCR-grid heuristic + optional RapidAI/VLM fallback on scanned pages)
tables = pdx.extract_tables("document.pdf")

# Key-value pairs from an invoice/form-like page (digital only)
fields = pdx.extract_kie("invoice.pdf")

# Any non-PDF format too — same call, same output shape
data = pdx.extract("spreadsheet.xlsx")
data = pdx.extract("scan.docx")

# Before processing a scanned document: check whether it's actually
# processable with what's currently installed, without running OCR.
report = pdx.can_process("document.pdf")
# {"n_pages": 12, "n_scanned_pages": 12, "needs_ocr": True,
#  "can_process": False, "problems": [...]}

page_workers > 1 (multi-page process-pool parallelism) uses multiprocessing's spawn start method — a caller script MUST guard its top-level code with if __name__ == "__main__":, or the worker processes die on import and every page silently falls back to sequential processing (you'll see a RuntimeWarning when this happens).

Each worker loads its own full OCR model set (~2.4GB RSS measured) — page_workers is automatically capped against both available CPU cores (respecting taskset/cgroup/Docker --cpus limits, not just total system cores) and available RAM, so a request for more workers than the machine can actually hold gets a smaller worker count instead of an OOM kill.

Selecting pages, features, and output format

pdx.extract("report.pdf", pages="1-3,7")           # specific pages
pdx.extract("report.pdf", feature="tables")         # just one feature, still full extract() under the hood
pdx.extract("report.pdf", output_format="markdown") # rendered markdown instead of the raw IR
pdx.extract("report.pdf", fields=True)               # turn on key-value extraction inline

Optional specialists (all off by default, all lazy-loaded)

pdx.extract("form.pdf", handwriting=True)   # TrOCR on handwritten blocks
pdx.extract("contract.pdf", signatures=True) # Conditional-DETR signature boxes
pdx.extract("paper.pdf", formulas=True)      # inline math -> LaTeX

None of these import their heavy dependencies unless the flag is set — import paradox2 alone never touches torch, paddle, or transformers.

The scanned-table router

Scanned pages route tables through three tiers, each opt-in past the first:

OCR-grid heuristic (free, always on)
        |  rejects merged/borderless/dense tables by design,
        |  or under-reads a real table (low_confidence flag)
        v
YOLO26 bbox (PARADOX2_YOLO_DETECT=1) -- crop the page to the detected
        |  table region before handing it to the tiers below. A
        |  borderline-confidence detection (<0.85) skips RapidAI
        |  entirely and goes straight to the VLM tier - VLM tolerates
        |  a loose/imprecise crop better than RapidAI's structure model
        v
RapidAI mid-tier (PARADOX2_RAPIDAI_TABLES=1)
        |  ONNX wired/wireless classifier + structure model. Its own
        |  result is cross-checked against OCR line density in the same
        |  crop (table_rapidai.py's low_confidence) - implausibly few
        |  OR implausibly many rows both re-route to the VLM tier
        v
VLM fallback (PARADOX2_VLM_TABLES=1)
        |  PaddleOCR-VL single-pass, reserved for tables that look
        |  genuinely complex - not run on everything RapidAI touches
        v
   best available result

Every tier is measured end-to-end (paradox2.extract(), not the isolated engine wrapper) on the same 20 real full-page tables (OmniDocBench), not simulated:

The isolated RapidAI engine alone scores 65% exact on tight ground-truth crops — the gap to the router's end-to-end number is the YOLO26 crop's margin versus a perfect crop, not a RapidAI accuracy problem. Cropping to the heuristic's own bbox instead of an independent detector actively hurts: that bbox only spans the rows the heuristic already found, so it bakes its own under-read into the pixels before the next tier ever sees them. A same-crop self-consistency check (RapidAI's row count vs. OCR line density) can't catch a loose crop itself, since both numbers inflate together on the same imprecise crop — that's what the YOLO-confidence trigger (<0.85) is for instead.

The heuristic never gets replaced by a worse result — a lower tier's output is kept unless a higher tier actually produces something.

export PARADOX2_YOLO_DETECT=1
export PARADOX2_RAPIDAI_TABLES=1
export PARADOX2_VLM_TABLES=1

Health check

from paradox2 import doctor
doctor()  # {"cuda": True/False, "pymupdf": "1.24.x", "ok": True/False, "problems": [...], ...}

paradox2 doctor          # full report
paradox2 doctor --fix    # prints only the pip install command(s) needed, exit 1 if any

First OCR call downloads model weights (PP-OCRv5, no progress bar) — a first extract() on a scanned page that appears to hang for 10-30s on a slow connection is this, not a crash. Backend stderr noise (onnxruntime/ TensorFlow/cuDNN registration warnings) is upstream noise from paddleocr's own dependencies, not paradox2 — there is currently no flag to suppress it.

Design principles

• Digital-first: a PDF with a text layer never pays for OCR, vision layout models, or GPU inference — verified per-page, not per-document.
• Facade stays thin: paradox2/api.py is dispatch only; business logic lives in pipeline/, tables/, engines/, formats/. If the facade creeps toward 100+ lines, that's a signal something leaked out of place.
• Specialists are lazy and swallow their own failures: an optional engine (handwriting, signatures, formulas, RapidAI, VLM) that fails to load or errors mid-call returns []/None — it never takes down the base extraction path.
• No simulated benchmarks: every accuracy/speed number in this README and in the codebase's docstrings comes from a real run against real documents, with the script path noted alongside it.

Status

Early-stage rewrite, private while the API and table router stabilize. See docs/PLAN.md for the phase breakdown and docs/TASKS.md for current work.