locale-invoice-check

A regression harness for invoice extraction. It renders the same synthetic invoices as PNG triplets in English, German, and Spanish, and changes only what locale conventions change: labels, numeric separators, date format, and how the euro sign sits next to the number. When accuracy drops between locales, locale handling is the only possible cause.

Fields in v0.1 (exact set): invoice_number, invoice_date, net_amount, tax_amount, total_amount.

Demo video

https://github.com/Gjusev/locale-invoice-check/releases/download/v0.1.1/brag.mp4

Why

OpenAI released GPT-6.1 Sol on September 29, 2026 with document understanding as a headline capability (announcement). Whether that survives a German decimal comma is what this harness measures.

Nothing here shows a defect in that or any model. The harness gives you controlled pairs and hard gates so you can measure your own extractors.

Quick start (no credentials)

pip install locale-invoice-check

locale-check generate fixtures --count 30 --seed 42
locale-check run fixtures --extractor regex --json --output result.json
locale-check demo

# strict demo: exits 1 (the bundled baseline intentionally fails DE/ES money)
# relaxed demo: exits 0
locale-check demo --min-field-accuracy 0 --max-locale-delta 1 --max-money-error-rate 1

## Extractors

Public contract: `extract(image_path, field_list) -> dict`, returning a
JSON-compatible mapping of `{field: value}` or `{field: {"value": ..., "confidence": ...}}`.

### RegexBaselineExtractor (offline, deterministic)

Reads the visible text transcription stored in the PNG `rendered_text` metadata
chunk. This is an OCR surrogate, not OCR: the harness tests parsing and locale
handling, so the package ships no OCR engine. Canonical ground truth never
touches that metadata. Only the displayed text is stored, and DE/ES renderings
use their locale separators, so canonical dot-decimals and ISO dates cannot leak.

The money parser in the baseline interprets every comma as a thousands
separator. That is the classic English-only bug, and it is in there on purpose.
On the default corpus (30 invoices, seed 42) the result is exactly what the bug
predicts: EN parses perfectly, DE and ES amounts fail. These numbers are measured:

| field_accuracy      | EN     | DE     | ES     |
|---------------------|--------|--------|--------|
| invoice_number      | 100.0% | 100.0% | 100.0% |
| invoice_date        | 100.0% | 100.0% | 100.0% |
| net_amount          | 100.0% | 0.0%   | 0.0%   |
| tax_amount          | 100.0% | 0.0%   | 0.0%   |
| total_amount        | 100.0% | 0.0%   | 0.0%   |

| rollup             | EN     | DE     | ES     |
|--------------------|--------|--------|--------|
| locale_accuracy    | 100.0% | 40.0%  | 40.0%  |
| money_error_rate   | 0.0%   | 100.0% | 100.0% |
| date_swap_rate     | 0.0%   | 0.0%   | 0.0%   |

`locale_delta` = −0.60. The strict demo exits **1**; the relaxed demo exits **0**.

### LLMExtractor (optional `[llm]` extra)

An OpenAI-compatible vision endpoint, image sent as a data URI, `temperature: 0`.
Only the image and field instructions are sent, never ground truth. Error
messages never include response bodies or credentials.

```bash
pip install "locale-invoice-check[llm]"
export VISION_API_KEY=...      # required
export VISION_MODEL=...        # required
export VISION_BASE_URL=https://api.openai.com/v1   # optional, this is the default
locale-check run fixtures --extractor llm

Requests and errors are tested offline via httpx.MockTransport. No live network test ships with the package.

Metrics (deterministic, no LLM judge)

With N = number of invoices and five fields:

• field_accuracy[locale][field] = correct fields / N (15 metrics, 5 fields x 3 locales)
• locale_accuracy[locale] = correct fields / (5·N)
• locale_delta = min(locale_accuracy) − locale_accuracy[EN]. Signed: a 12-point drop is -0.12
• money_error_rate[locale] = invoices with at least one wrong amount / N
• date_swap_rate[locale] = invoices with an observable day/month inversion / N

Date swap observability

A day/month inversion can only hide when day ≤ 12 and day ≠ month. In any other case the swapped reading is either invalid or identical. For invoices where the inversion is not observable, the swap rate cannot count it; a plain date mismatch still fails the field.

Amount and date normalization

Formatting is normalized. Wrong values are never repaired.

• IDs: equality after strip().
• Dates: YYYY-MM-DD, DD.MM.YYYY, DD/MM/YYYY → ISO YYYY-MM-DD. The slash format reads day-first (Spanish convention), as does the dot format.
• Amounts use Decimal with an inclusive absolute tolerance of 0.01:
  • currency symbols (€, EUR), spaces, and NBSP are stripped;
  • both . and , present → the rightmost one is the decimal separator;
  • only , → a trailing group of exactly 3 digits means thousands (1,234 → 1234); otherwise decimal (9,90 → 9.90);
  • only . → multiple dots mean thousands; a single dot is read as decimal (12.000 → 12.0). This is a documented bias toward the EN reading. Some ambiguous cases are inherently undecidable and are normalized, not repaired;
  • consecutive separators (1..2) are rejected.

Gates

Exit codes

A transport failure (network down, HTTP 5xx, invalid JSON from the vision endpoint) is always exit 2. It is never silently turned into an extraction failure.

Artifacts

• result.json holds the full run: metrics, gates, and per-invoice/per-field details, with schema_version: "1.0". A JSON Schema ships inside the package (locale_invoice_check/schemas/result.v1.json, locale_invoice_check.load_result_schema()). Changes within schema version 1.0 are additive only.
• review-queue.json is {schema_version, review_queue}. Every failed or low-confidence field lists invoice, locale, field, expected, got, and reasons. Confidence comes only from the adapter; the harness never invents it.
• report.html is standalone. No server, no remote resources: one row per invoice, EN/DE/ES columns with base64 thumbnails, per-field expected/got, failures in red, and metrics, gates, and the review queue at the end. All external content is HTML-escaped.

Python API

from locale_invoice_check import (FIELDS, LOCALES, LLMExtractor,
                                  RegexBaselineExtractor, evaluate_corpus,
                                  generate_corpus, load_corpus)

generate_corpus("fixtures", count=30, seed=42)          # 30 x EN/DE/ES PNGs
_, manifest = load_corpus("fixtures")                    # validated, hash-checked
result = evaluate_corpus("fixtures", RegexBaselineExtractor())
result["passed"]            # bool
result["metrics"]["field_accuracy"]["DE"]["net_amount"]

python -m locale_invoice_check works wherever the console script is not on your PATH.

Reproducibility

• Generation is seeded (--seed). The manifest records seed, renderer configuration (Pillow version), relative image paths, and SHA-256 hashes. Loading a corpus verifies every hash, so a corpus and its results are redistributable and bit-reproducible.
• Results contain no timestamps or absolute paths. Runs are deterministic offline.
• The corpus is entirely synthetic: no real invoices, no personal data. It is Apache-2.0 licensed with the code.

Reproduce in Kaggle

The kernel installs locale-invoice-check==0.1.2 from PyPI, so this requires the package to be published on PyPI first.

kaggle-kernel/offline/script.py plus kaggle-kernel/kernel-metadata.json define a public CPU kernel (enable_gpu: false, enable_internet: true; internet is only needed for the pip install). It runs the offline demo with relaxed gates, which are explicitly not recommended production thresholds, prints a summary, and writes result.json, report.html, and review-queue.json to /kaggle/working.

Authenticate with a Kaggle API token (never commit it):

export KAGGLE_API_TOKEN=<your-token>   # official OAuth token variable
kaggle kernels push -p kaggle-kernel
kaggle kernels status gjusev/locale-invoice-check-demo
kaggle kernels output gjusev/locale-invoice-check-demo -p ./kernel-output

Related benchmarks (by name, not instead of)

Real document-extraction benchmarks exist, and they are more representative of production traffic than this corpus:

• DocILE (ICDAR 2023, Rossum) has ~6.7k annotated business documents as PDFs for key information localization and line-item recognition. Large and realistic, but the documents are not locale-controlled matched pairs.
• DocuBench (DocuPipe) scores real-world documents with JSON schemas and hand-verified labels on macro-average field accuracy.
• Omni Extract Bench (Datalab) is an extraction benchmark toolkit with provider prediction harnesses and interpretable scoring.

What this package adds is narrower: controlled pairs where only locale conventions change, full offline determinism, redistributable synthetic data, and gate semantics with exit codes for CI. It is not a leaderboard, and its absolute numbers say nothing about real-world accuracy.

Limitations

• Synthetic layout. A real OCR pipeline is out of scope; the baseline reads rendered_text metadata by design.
• Only PNG, only EUR, only the five v0.1 fields, only EN/DE/ES.
• Amount/date normalization resolves ambiguous separators with documented heuristics. Inherently ambiguous inputs are normalized, not repaired.
• Date-swap detection cannot observe inversions when day ≤ 12 is violated.
• Confidence comes only from adapters that provide it.

Development

make install   # uv sync
make test      # pytest, network hard-blocked
make lint      # ruff (E, F, I, UP, RUF; line-length 100)
make build
make demo      # strict demo must exit 1, relaxed must exit 0

CI (.github/workflows/test.yml) runs Ruff and offline pytest on Python 3.10-3.13, asserts the relaxed demo exits 0, builds the wheel, and smoke-tests it in a clean venv. Publishing (.github/workflows/publish.yml) uses uv build and uv publish via Trusted Publishing on release: published. No tokens are stored.

License

Apache-2.0