German Language Support

Domain: Healthcare, Legal, Finance, General Data Type: German-language free text (medical documents, contracts, invoices, ID documents) Goal: Detect German PII and sensitive identifiers using Presidio's built-in German recognizers with a bilingual spaCy NLP engine

Overview

Domain: Healthcare / Legal / Finance Data Type: German-language documents Goal: Detect German PII — including healthcare identifiers, tax numbers, official document numbers, and vehicle plates — using the German spaCy model alongside the English model so that bilingual (EN + DE) documents are handled correctly.

This recipe provides:

• spacy_en_de.yaml — a ready-to-use NLP engine configuration that loads both en_core_web_lg and de_core_news_md
• An overview of all German-specific recognizers available in Presidio

Quick Start

Prerequisites

pip install presidio-analyzer presidio-anonymizer
python -m spacy download en_core_web_lg
python -m spacy download de_core_news_md

Sample Data

sample_text = """
Sehr geehrter Herr Müller,

Ihre Krankenversicherungsnummer (KVNR): A123456780
Steuer-IdNr.: 86095742719
Arztnummer (LANR): 123456601
Betriebsstättennummer (BSNR): 021234568
USt-IdNr.: DE136695976
Führerscheinnummer: BO12345678A
Reisepassnummer: C01234565
"""

Basic Configuration

from presidio_analyzer import AnalyzerEngine
from presidio_analyzer.nlp_engine import NlpEngineProvider
from presidio_anonymizer import AnonymizerEngine

# Load the bilingual EN + DE spaCy configuration
provider = NlpEngineProvider(conf_file="spacy_en_de.yaml")
nlp_engine = provider.create_engine()

analyzer = AnalyzerEngine(
    nlp_engine=nlp_engine,
    supported_languages=["en", "de"],
)
anonymizer = AnonymizerEngine()

# Analyze German text
results = analyzer.analyze(text=sample_text, language="de")
anonymized = anonymizer.anonymize(text=sample_text, analyzer_results=results)

print(anonymized.text)

Approach

Presidio ships pattern-based recognizers for 13 German entity types (see table below). Each recognizer targets a single entity, uses \b-anchored regex patterns with base confidence between 0.2 and 0.5, and relies on:

1. Context words (German terminology near the match) to boost confidence
2. Check digit validation where the official specification defines one (KVNR, Rentenversicherungsnummer, Steuer-IdNr., LANR)

The spaCy de_core_news_md model adds named-entity recognition for PERSON, LOCATION, and ORGANIZATION on top of the pattern recognizers.

Supported German Entities

Results

Formal evaluation against a labelled German dataset has not yet been performed. To benchmark this recipe follow the Presidio Research evaluation workflow:

1. Generate synthetic German text with the data generator
2. Configure the analyzer with spacy_en_de.yaml
3. Run the evaluator and report precision / recall / F₂ / latency

Precision: TBD Recall: TBD F₂ Score: TBD Latency: TBD

Key Findings

• Recognizers with check digit validation (KVNR, RVNR, Steuer-IdNr., LANR) achieve very low false-positive rates even on ambiguous digit strings.
• Recognizers without a checksum (BSNR, PLZ, KFZ) rely heavily on context words; setting score_threshold=0.5 when no context is present is recommended.
• The DE_PLZ (postal code) and DE_KFZ (vehicle plate) recognizers overlap with generic patterns; use the entities parameter to restrict detection when only specific entity types are needed.

Tips for Others

• Set score_threshold to 0.4–0.5 for production use to filter out low-confidence pattern-only matches from context-free digit strings.
• Use the entities parameter to limit detection to the entity types relevant to your domain (e.g. only healthcare identifiers in clinical notes).
• Pre-2013 Führerschein numbers use locally defined, non-standardized formats that are not covered by DE_FUEHRERSCHEIN; handle them with a custom recognizer if needed.
• DE_TELEMATIK_ID was evaluated and rejected as a generic recognizer: the format (\d{1,2}-<up to 128 chars>) is too ambiguous for reliable free-text detection.

Author: MvdB Date: 2026-03-19