Skip to main content

Offline document anonymizer for legal teams

Project description

anonymizer

Offline document anonymizer for legal teams. Replaces personally identifiable information (PII) in documents with structured tokens before sending them to external AI services.

Status: MVP-0 release candidate.

What it does

Drag a file (docx / xlsx / pdf, including scanned PDFs when local OCR is available) into the local web UI and get an anonymized document where:

  • Names, companies, financial details, addresses, emails, phones are replaced with structured tokens like [Person_1], [Company_1], [ADDRESS_1], ...
  • Document metadata is cleared
  • No network calls during processing — runs entirely on your machine

Then send the result to your AI tool of choice.

MVP-0 scope

  • Formats: docx, xlsx, pdf with text layer, scanned PDF, and hybrid PDF
  • Languages: Russian, English (NER); language-agnostic detectors for emails, phones, IBAN, cards, IP/MAC/URL, dates, geocoordinates
  • Platforms: Windows + macOS
  • UI: local web app at 127.0.0.1 in your browser
  • Install: single curl one-liner → uv tool install docs-anonymizer

Scanned and hybrid PDFs use local Tesseract OCR with English and Russian language packs. Password-protected files, additional languages, and editable recognized-DOCX export remain planned for later iterations.

Installation

# macOS / Linux
curl -fsSL https://anonymizer.site/install.sh | sh

# Windows (PowerShell)
iwr -useb https://anonymizer.site/install.ps1 | iex

Then run anonymize — your browser will open at http://127.0.0.1:<port>.

OCR setup for scanned PDFs

Scanned and hybrid PDFs require system Tesseract with English and Russian language packs. The anonymizer installer offers to install Tesseract interactively and shows an approximate download/install size before asking. If you skip it, DOCX, XLSX, and PDFs with a text layer still work.

# macOS
brew install tesseract tesseract-lang

# Ubuntu / Debian
sudo apt install tesseract-ocr tesseract-ocr-eng tesseract-ocr-rus

# Windows (PowerShell)
winget install UB-Mannheim.TesseractOCR

On macOS, Homebrew's tesseract-lang package is large because it bundles all extra languages; expect up to roughly 720 MB on disk. Ubuntu/Debian and Windows downloads are usually smaller, and the package manager may show the exact download size.

After installing Tesseract, run:

anonymize doctor --no-network

If OCR is unavailable, scanned PDF processing is rejected with installation guidance instead of silently skipping scanned pages.

Stack

Python 3.11+, FastAPI + htmx, spaCy + Natasha, PyMuPDF, python-docx, openpyxl, lxml. Full details in the technical spec.

Architecture

Three-layer design — core (headless Python library), cli, webapp (FastAPI on loopback) — plus testkit for synthetic test corpus generation and feedback loop tooling. Detectors are pluggable; language packs are drop-in. Manual masking + audit logging without PII leakage.

Licenses

The project is released under AGPL-3.0 because it depends on PyMuPDF (AGPL). All other dependencies are permissive open-source (MIT / Apache 2.0 / BSD / MPL). The source distribution published with each release contains the project source needed to satisfy AGPL source-availability obligations.

A page in the application UI will list all bundled libraries and models with their individual licenses.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

docs_anonymizer-0.2.29.tar.gz (799.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

docs_anonymizer-0.2.29-py3-none-any.whl (298.6 kB view details)

Uploaded Python 3

File details

Details for the file docs_anonymizer-0.2.29.tar.gz.

File metadata

  • Download URL: docs_anonymizer-0.2.29.tar.gz
  • Upload date:
  • Size: 799.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.9.5

File hashes

Hashes for docs_anonymizer-0.2.29.tar.gz
Algorithm Hash digest
SHA256 c0ac52a2377dce5b5d3e557b65f41b755047bbc71ee717ea9cdb0f4d0b4573ad
MD5 7d4d09a30cd1593526bec24105caff82
BLAKE2b-256 a47f7b676749c6fdc9414074ae513480f315b3fa0c7470003ac7e7d80c4fee18

See more details on using hashes here.

File details

Details for the file docs_anonymizer-0.2.29-py3-none-any.whl.

File metadata

File hashes

Hashes for docs_anonymizer-0.2.29-py3-none-any.whl
Algorithm Hash digest
SHA256 cf7308e021baebadd0f7c7d0f087045ba9f217577a8e984fc71a6a9dee9c00b3
MD5 acdf1a2d2de23fa54dd6c887a5b4b509
BLAKE2b-256 6c2e717c1ca1306f30f4328a8bfd9300d3d6ca42c52b67894e401b2c724fc39c

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page