hankpdf 0.2.0

Aggressive, safety-first PDF shrinker for scanned documents. CLI tool: PDF in, shrunk searchable PDF out.

HankPDF

Aggressive, safety-first PDF shrinker for scanned documents. Takes a PDF in, produces a shrunk/resized searchable PDF out. Local tool. No network, no telemetry, no data leaves your machine. Targets 8–15× typical compression (up to 200× on text-dominant scans) while preserving OCR searchability and guaranteeing no silent content loss.

Status: v0.2.0 — 393 tests passing on Linux / macOS / Windows CI. Available via PyPI (pip install hankpdf), GHCR (docker pull ghcr.io/hank-ai/hankpdf:v0.2.0), or from the repo (see Setup below).

v0.2.0 rename note: the PyPI distribution and import package were renamed from pdf-smasher to hankpdf so the install name matches the CLI command, GHCR image, and product brand. The legacy pdf_smasher import package still works for one cycle (emits a DeprecationWarning pointing at hankpdf); removal in v0.3.0. The yanked pdf-smasher 0.1.0 on PyPI continues to install at the exact pin (PEP 592) but bare pip install pdf-smasher no longer resolves.

⚠️ Required: native binaries (pip install is NOT enough)

HankPDF is a Python wrapper around three native CLI tools. pip install hankpdf does NOT install them — you have to do it via your system package manager BEFORE the wrapper works, or use the Docker image which has them baked in.

Binary	Why	Without it
Tesseract	OCR text-layer extraction + verifier	hankpdf exits with tesseract is not installed or it's not in your PATH on any input that needs OCR
qpdf	structural repair + linearization	EnvironmentError, refuses to run
jbig2enc	text-region encoder for the MRC pipeline	text-only compression drops from ~50× to ~6× (flate fallback); searchable PDFs still produced

Install them via your OS package manager (brew install tesseract qpdf on macOS, apt install tesseract-ocr qpdf jbig2enc-tools on Debian/Ubuntu, choco install tesseract qpdf on Windows). Build jbig2enc from source where it isn't packaged — see docs/INSTALL.md for full per-OS instructions including the Windows jbig2.exe installer.

After install, run hankpdf --doctor — it prints the version of every binary it can find and NOT FOUND for any that's missing. Run it before you trust any other output.

Want zero setup? Use the Docker image (docker pull ghcr.io/hank-ai/hankpdf:v0.1.0) — Tesseract, qpdf, and jbig2enc are all baked in. See Setup → Docker below.

What it does

Takes oversized scanned PDFs (typical input: 200-page, 800 MB image scans) and produces compact, searchable, verified outputs. CLI-first. Two install targets, both run the same engine locally:

1. Python package — pip install hankpdf (gives you the compress() API and the hankpdf console script) plus Tesseract + qpdf + jbig2enc via your system package manager. See the loud callout above and docs/INSTALL.md for one-line per-OS install instructions.
2. Docker image — ghcr.io/hank-ai/hankpdf:latest. All native deps baked in; zero host setup. Ideal for CI/CD, SFTP upload wrappers, batch jobs, and any environment where installing Tesseract on the host is inconvenient.

Not a service, not a GUI, not a signed installer. HankPDF is a command-line tool. It runs entirely on the user's machine, never uploads PDFs anywhere, never phones home, writes no analytics, stores no persistent state beyond what the user asks (output PDF, optional sidecar manifest).

What makes it different

• Permissive license throughout — no AGPL, no commercial SDK dependency. Built entirely on Apache-2.0 / BSD / MPL-2.0 components. We can ship, modify, and redistribute freely.
• Content-preservation invariant — every output is gated by OCR-text diff, tile-level SSIM, and structural audit. We refuse rather than silently corrupt.
• Weird-PDF robust — encrypted, signed, corrupt-xref, JBIG2-in, form XObjects, color profiles, linearized, tagged, PDF/A-3-embedded: each class has an explicit detect-and-handle policy. None crash the pipeline.
• Honest compression targets — for scanned-document inputs, we deliver ≥3× guaranteed, 8–15× typical, 50–200× best-case on text-only scans. For PDFs that are already efficiently encoded (vector slide decks, presentations, native exports from Word/Powerpoint), the MRC re-rasterize-and-recompress pipeline can produce larger output — so the default --min-ratio 1.5 short-circuits to passthrough rather than churning, and text-only inputs now passthrough even faster via the per-page MRC gate (see below). See docs/PERFORMANCE.md for measured ratios across input types and settings.

Defaults preserve any existing text layer (byte-faithful to the source, no flag required). --ocr means ensure searchable — it runs Tesseract only on pages where the input has no text or the existing text fails a quality heuristic. --strip-text-layer opts out (text-free output); --re-ocr forces Tesseract everywhere even when the input has good native text. See docs/PERFORMANCE.md for measured behavior across the four scenarios.

Output modes

HankPDF produces three output shapes out of the same hankpdf command:

PDF (default):

hankpdf in.pdf -o out.pdf

Chunked PDF (for email attachment limits):

hankpdf in.pdf -o out.pdf --max-output-mb 25
# writes out_001.pdf, out_002.pdf, ... if the merged output exceeds 25 MB

Chunk filenames are zero-padded, 1-indexed, and preserve page order. A single page that's already larger than the cap is emitted alone (you'll see a stderr warning).

Scheme change in schema v2: chunk filenames are now {base}_{NNN}{ext} (1-indexed, min 3-digit zero-pad). The previous scheme was {base}_{idx}{ext} (0-indexed, unpadded). Automation pinned to out_0.pdf should migrate to out_001.pdf or glob out_*.pdf with a lexical sort.

Per-page image export (JPEG, PNG, or WebP):

hankpdf in.pdf -o page.jpg --pages 1 --image-dpi 150 --jpeg-quality 80
hankpdf in.pdf -o dump.png --image-dpi 200
hankpdf in.pdf -o small.webp --pages 1-5 --webp-quality 70

Image export skips the MRC compression pipeline; each requested page is rendered and saved as a standalone image. The output format is inferred from the -o extension (or set explicitly via --output-format). Use --pages to restrict to a subset — without it, every page is exported.

Passthrough on low compressibility

By default HankPDF returns the input unchanged if the achieved ratio is below 1.5× — producing an MRC output larger than the input serves no one. The run exits 0 with status="passed_through" (exit code 2) and a warning code passthrough-ratio-floor on stderr; report.output_sha256 equals report.input_sha256 and the verifier is marked "skipped" with fail-closed sentinels so downstream gates can't mistake passthrough for a clean verified run.

Overrides:

hankpdf in.pdf -o out.pdf --min-ratio 1.0   # force MRC output regardless
hankpdf in.pdf -o out.pdf --min-ratio 0     # disable the floor entirely
hankpdf in.pdf -o out.pdf --min-input-mb 5  # also passthrough if input < 5 MB

--min-input-mb is a sibling gate for inputs so small that the MRC per-page overhead (~2-3 s/page) isn't worth the ratio gain; it emits warning code passthrough-min-input-mb.

A third gate runs before both of those: a per-page MRC-worthiness classifier that skips the pipeline entirely on pages with no meaningful image content. For each page, image_xobject_bytes / page_byte_budget is compared against --per-page-min-image-fraction (default 0.30); pages below the threshold are emitted verbatim (no rasterize, no compose, no verify). When no page meets the threshold the whole-doc shortcut returns the input bytes unchanged with warning code passthrough-no-image-content; partial runs emit pages-skipped-verbatim-N. The gate is bypassed by --re-ocr, --strip-text-layer, --legal-mode, --verify, or --per-page-min-image-fraction 0. See docs/PERFORMANCE.md "Per-page MRC gate" for measurements.

Setup

Hand this repo to Claude Code / Codex / any coding agent

If you're not a developer, the fastest path is to clone the repo, open it in an agent-capable editor (Claude Code, Cursor, Copilot, etc.), and paste this prompt:

Read README.md and docs/INSTALL.md. Detect my operating system. Install every native dependency HankPDF needs (Python 3.14, uv, Tesseract, qpdf, jbig2enc), clone any missing binaries, run uv sync --all-extras, then run uv run pytest -q and report the result. If any step needs my input (sudo password, GitHub auth, WSL activation), stop and tell me.

The agent reads the OS-specific blocks below, runs the commands, reports test output. You'll be up in ~5-15 minutes depending on network and whether jbig2enc needs building from source.

Docker (any OS)

Zero host setup. Works on macOS, Linux, and Windows with Docker Desktop.

docker pull ghcr.io/hank-ai/hankpdf:latest

# macOS / Windows (Docker Desktop handles uid mapping):
docker run --rm -v "$PWD:/data" ghcr.io/hank-ai/hankpdf:latest \
    /data/in.pdf -o /data/out.pdf

# Linux (pass -u so the container can write to your bind mount):
docker run --rm -u "$(id -u):$(id -g)" -v "$PWD:/data" \
    ghcr.io/hank-ai/hankpdf:latest \
    /data/in.pdf -o /data/out.pdf

For production use, pin to an immutable tag or digest:

# Immutable — a specific release:
docker pull ghcr.io/hank-ai/hankpdf:<version-tag>

# Immutable — a specific commit SHA:
docker pull ghcr.io/hank-ai/hankpdf@sha256:<digest>

# `:latest` is MUTABLE — it floats with every main-branch merge.
# Fine for local dev; never for production batch jobs where you
# want to know exactly what bytes ran.

Every pushed image is signed with cosign (keyless, via GitHub OIDC) and carries a SLSA v1 build-provenance attestation. Verify before running in production:

cosign verify ghcr.io/hank-ai/hankpdf:<version-tag> \
    --certificate-identity-regexp 'https://github\.com/hank-ai/(hankpdf|pdf-smasher)/\.github/workflows/docker\.yml@refs/(heads|tags)/.+' \
    --certificate-oidc-issuer https://token.actions.githubusercontent.com

See docker/README.md for tag semantics, uid rationale, and local-build instructions.

Manual setup — macOS

# 1. Python 3.14 + uv
brew install python@3.14
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. Native deps (tesseract + qpdf via brew; jbig2enc from source)
brew install tesseract qpdf
git clone --depth=1 https://github.com/agl/jbig2enc.git /tmp/jbig2enc
cd /tmp/jbig2enc && ./autogen.sh && ./configure && make && sudo make install

# 3. HankPDF
git clone [e-mail]:hank-ai/hankpdf.git
cd hankpdf
uv sync --all-extras
uv run hankpdf --version       # smoke test
uv run pytest -q               # full test suite (~1 min)

Manual setup — Linux (Debian / Ubuntu)

# 1. Python 3.14 (deadsnakes PPA on Ubuntu <25.04)
sudo add-apt-repository -y ppa:deadsnakes/ppa
sudo apt update
sudo apt install -y python3.14 python3.14-venv python3.14-dev

# 2. uv
curl -LsSf https://astral.sh/uv/install.sh | sh
source "$HOME/.local/bin/env"

# 3. Native deps
sudo apt install -y tesseract-ocr libtesseract-dev qpdf jbig2enc-tools

# 4. HankPDF
git clone [e-mail]:hank-ai/hankpdf.git
cd hankpdf
uv sync --all-extras
uv run hankpdf --version
uv run pytest -q

(Fedora/RHEL: swap apt install for dnf install -y tesseract qpdf leptonica-devel; jbig2enc still needs building from source per docs/INSTALL.md.)

Manual setup — Windows

Three paths. Pick Docker unless you need a native Python install.

Option 0 — Docker Desktop (easiest for non-developers):

# Install Docker Desktop from docker.com
docker pull ghcr.io/hank-ai/hankpdf:latest
docker run --rm -v "${PWD}:/data" ghcr.io/hank-ai/hankpdf:latest `
    /data/in.pdf -o /data/out.pdf

No Python, no native deps, no WSL needed. Works on Windows 10/11 Pro/Enterprise/Home with WSL2 backend enabled. Docker Desktop licensing is free for personal use, education, and small businesses (<= 250 employees and < $10M annual revenue). Larger orgs need a Docker Business license or can install Docker Engine via WSL instead.

Option A — WSL2 (recommended): run the Linux instructions above inside WSL Ubuntu. From PowerShell (admin):

wsl --install -d Ubuntu-24.04

Reboot, then follow the Linux block inside the Ubuntu shell. Your Windows files are at /mnt/c/Users/<YourUser>/....

Option B — Native Windows (PowerShell):

winget install Python.Python.3.14
choco install tesseract qpdf -y
irm https://astral.sh/uv/install.ps1 | iex

# Install jbig2.exe for full MRC compression (optional; CCITT G4 fallback works without it).
# Tagged URL — main is a mutable branch, so we pin to a specific release.
# Replace the tag with whatever "jbig2-windows-v*" tag you want to install.
$tag = "jbig2-windows-v0.1.0"
irm "https://github.com/hank-ai/hankpdf/releases/download/$tag/install_jbig2_windows.ps1" | iex

git clone [e-mail]:hank-ai/hankpdf.git
cd hankpdf
uv sync --all-extras
uv run hankpdf --version
uv run pytest -q

The jbig2 installer pulls a prebuilt jbig2.exe (plus its runtime DLLs) from the hankpdf GitHub Releases, extracts it to %LOCALAPPDATA%\hankpdf\bin, and registers that directory on your user PATH. No administrator required. Open a new terminal after the installer runs so the PATH update is picked up. Source and build recipe live in scripts/install_jbig2_windows.ps1 and .github/workflows/windows-jbig2enc.yml.

Without the jbig2 installer, the MRC pipeline falls back to CCITT G4 for the text layer — outputs are typically 10-20% larger than with jbig2enc, but every other feature works identically and all tests pass.

Put hankpdf on your PATH

After uv sync succeeds, you can either keep prefixing with uv run hankpdf, or install the console script system-wide:

uv tool install --from . hankpdf
hankpdf --version

Running tests

uv run pytest -q                          # all 393 tests (~1 min)
uv run pytest tests/unit -v               # unit only (~10 s)
uv run pytest -m integration -v           # integration only
uv run pytest --cov=hankpdf           # with coverage
uv run ruff check hankpdf tests       # lint
uv run mypy hankpdf                   # type check

Troubleshooting

• hankpdf --version — prints Python version, hankpdf version, and every native dep's version + path. If one is missing, that's your install problem.
• uv run python -c "import hankpdf; print('OK')" — import smoke test.
• OCR unit tests auto-skip when tesseract isn't on PATH. The rest of the suite should pass regardless.
• Still stuck? Open an issue with hankpdf --version output attached.

Documentation

Doc	Purpose
docs/ARCHITECTURE.md	Design decisions, rationale, system diagram. The why.
docs/SPEC.md	Functional spec — CLI contract, API surface, behaviors, edge-case policies. The what.
docs/PERFORMANCE.md	Measured compression ratios + wall-time across input types and settings. The how-fast and how-small.
docs/KNOWLEDGE.md	Reference material: MRC algorithm, codec trade-offs, license notes, PDF internals, prior-art summaries. The background.
docs/ROADMAP.md	Phased implementation checklist. The how and when.

License

HankPDF is licensed under Apache-2.0 (LICENSE).

Commercial use

HankPDF is cleared for commercial use. The dependency tree was chosen specifically to avoid the commercial-licensing blockers common in PDF/OCR tooling (Ghostscript AGPL, Poppler GPL, ABBYY/Nuance per-seat licensing).

See THIRD_PARTY_LICENSES.md for the full dependency audit — every Python package, native binary, and transitive system library with its SPDX identifier and commercial-use status. That file is the canonical reference for any licensing question; re-audit before every release.

Attribution for bundled/runtime third-party code is in NOTICE.