lx-anonymizer 0.8.8.1

OCR-driven anonymization pipeline for medical reports and endoscopy frames

LX Anonymizer

LX Anonymizer is a toolkit for de-identifying endoscopy frames and medical reports. It combines OCR pipelines, spaCy-based NER, heuristic sanitizers, and report-specific rules to redact or pseudonymize sensitive information while preserving clinical context.

Highlights

• End-to-end anonymization of PDFs and frame sequences using OCR, NER, and pseudonymization helpers.
• Modular pipeline that lets you choose between Tesseract, TrOCR, ensemble OCR, and multiple metadata extractors.
• Human-in-the-loop ready outputs: original/anonymized text side by side, metadata JSON, and validation artefacts.
• Extensible ruleset covering device-specific renderers, fuzzy name matching, and language-specific replacements.

Requirements

• Python 3.12+
• Linux or macOS (Windows support is experimental)
• NVIDIA GPU recommended for real-time video anonymization (CUDA 12.x). CPU-only processing works but is slower.
• Optional extras:
  • spaCy de_core_news_lg model (download after installation)
  • Torch vision/audio for video OCR workloads
  • Ollama-compatible LLMs for advanced metadata extraction

Installation

From PyPI (upcoming release)

pip install lx-anonymizer

Install extras to tailor the footprint:

pip install "lx-anonymizer[gpu,ocr,llm,dev]"

From source

git clone https://github.com/wg-lux/lx-anonymizer.git
cd lx-anonymizer
uv sync

Nix development shell

direnv allow
nix develop

This loads GPU, OCR, and tooling dependencies declared in devenv.nix.

Model downloads

After installation, fetch the German spaCy model:

python -m spacy download de_core_news_lg

First CLI runs also download OCR checkpoints (EAST, TrOCR, etc.). For air-gapped deployments, grab the archives listed in lx_anonymizer/settings.py and place them in ~/.cache/lx-anonymizer.

Quickstart

CLI

python -m cli.report_reader process report.pdf --ensemble --output-dir ./anonymized

Useful options:

• --llm-extractor {deepseek,medllama,llama3} for LLM-powered metadata extraction.
• --use-ocr and --ensemble to switch OCR strategies.
• batch and extract sub-commands for folder processing or metadata-only runs.

Python API

from lx_anonymizer import ReportReader

reader = ReportReader(locale="de_DE")
original, anonymized, meta = reader.process_report(
    pdf_path="/path/to/report.pdf",
    use_ensemble=True,
    use_llm_extractor="deepseek",
)

See tests/test_cli_integration.py for more examples.

Data directories

By default, outputs live in ~/etc/lx-anonymizer/{data,temp}. Adjust them in lx_anonymizer/directory_setup.py. Clean temp regularly to avoid large intermediate artefacts.

Development workflow

• Format & lint: uv run flake8
• Tests (CPU friendly): uv run pytest -m "not gpu"
  • GPU tests are marked and can be run with -m gpu
• Build wheel for release: uv run python -m build
• Full local check helper: scripts/run_checks.sh

Project roadmap

1. Publish CPU-only wheel to TestPyPI.
2. Add optional extras for GPU/LLM workloads and slim default install.
3. Automate release workflow (wheel + sdist upload, GitHub release notes).
4. Expose REST/gRPC service with validation UI.

Contributing

See CONTRIBUTING.md for contribution guidelines, testing instructions, and communication channels.

License

Released under the MIT License.

Contact

Questions? Email [e-mail] .