cartographer-filings 0.1.1

High-fidelity PDF-to-markdown pipeline for financial filings. Multilingual, jurisdiction-aware.

Cartographer

High-fidelity PDF-to-markdown pipeline for financial filings. Multilingual, jurisdiction-aware, deterministic.

Cartographer takes annual reports, quarterly filings, and regulatory documents — and produces structured markdown plus classified sections (Income Statement, Balance Sheet, Cash Flow, MD&A, Risk Factors, numbered Notes) that downstream pipelines can consume directly. It works across 9 languages and 10+ jurisdictions, from SEC 10-Ks to ESEF annual reports to HKEX filings.

Why it exists

Commercial PDF-to-markdown services for financial documents are expensive, opaque, and often miss the structural cues that matter most: note numbering hierarchies, table reconstruction from broken cells, multi-column layouts, language-specific section headers. Cartographer is deterministic — regex and structural rules do the cleanup, LLMs are only used as an optional fallback for image-heavy pages. Every transformation is auditable. In internal benchmarks across 6 jurisdictions, it outperformed a commercial baseline on deterministic structural metrics.

Quickstart

pip install cartographer-filings

from cartographer import Pipeline

pipe = Pipeline()
result = pipe.extract("annual_report.pdf")

print(result["metadata"])
# {"company": "Siemens AG", "currency": "EUR", "fiscal_year": 2025, "standard": "IFRS", ...}

print(f"{len(result['notes'])} notes across {result['stats']['sections_found']} classified sections")
print(result["sections"]["income_statement"][:500])

Pipeline

flowchart LR
    PDF[PDF file] --> RAW[pymupdf4llm<br/>raw markdown]
    RAW --> ENH[enhance<br/>tables • headings • noise]
    ENH --> CLEAN[clean markdown]
    CLEAN --> PARSE[parser<br/>sections + notes]
    PARSE --> OUT[structured dict]
    ENH -.optional.-> VIS[Qwen-VL<br/>image-heavy pages]
    VIS -.-> CLEAN

Three deterministic stages plus one optional LLM fallback:

• enhance — reconstructs tables from <br>-stacked cells, promotes bold runs to headings, strips repeated page headers/footers, normalises negative number formats ((1,234) → -1,234).
• parser — classifies sections (IS, BS, CF, MD&A, Risk, Audit), detects numbered notes with V5 type mapping, extracts metadata (company, currency, fiscal year, reporting standard) across 9 languages.
• vision — only triggered on image-heavy pages where text extraction yields nothing useful. Uses Qwen-VL via SiliconFlow. Opt-in via API key.

Output schema

{
    "markdown": str,                  # Enhanced markdown, full document
    "metadata": {
        "company": str | None,
        "currency": str | None,       # ISO 4217 code
        "fiscal_year": int | None,
        "period_end": str | None,     # ISO date
        "standard": str | None,       # IFRS, US-GAAP, K-IFRS, ...
        "language": str | None,
    },
    "sections": {
        "income_statement": str | None,
        "balance_sheet": str | None,
        "cash_flow": str | None,
        "mda": str | None,
        "risk": str | None,
        "audit": str | None,
    },
    "notes": [
        {
            "note_number": int,
            "title": str,
            "content": str,
            "v5_type": str | None,    # Classified note type (e.g. U03, Related Parties)
            "start_char": int,
            "end_char": int,
        },
        ...
    ],
    "stats": {
        "pages": int,
        "raw_chars": int,
        "enhanced_chars": int,
        "sections_found": int,
        "notes_count": int,
        "vision_pages": int,
        "time_seconds": float,
    },
}

Vision fallback

Image-heavy pages (scanned filings, heavy-graphic annual reports) are only handled when you provide a SiliconFlow API key:

from cartographer import Pipeline

pipe = Pipeline(vision_api_key="sk-...")  # or SILICONFLOW_API_KEY env var
result = pipe.extract("scanned_report.pdf")

Vision is a fallback, not a default path. The overwhelming majority of financial filings parse correctly through the deterministic pipeline alone.

Supported languages

English, German, French, Spanish, Italian, Portuguese, Korean, Japanese, Chinese (partial).

Jurisdictions tested

Region	Standard	Examples
United States	US-GAAP	SEC 10-K, 10-Q
Germany	IFRS	ESEF filings, Bundesanzeiger
Italy	IFRS	CONSOB / SDIR
Portugal	IFRS	CMVM filings
Korea	K-IFRS	DART filings
Hong Kong	IFRS (HK)	HKEXnews
Australia	IFRS	ASX listings

Development

git clone https://github.com/hugocondesa-debug/cartographer.git
cd cartographer
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pytest tests/ -v
ruff check src/ tests/

See CONTRIBUTING.md for details.

Roadmap

• PyPI release (pip install cartographer-filings)
• Regression test suite fetching from primary sources (SEC, ESEF, HKEXnews)
• Thai, Arabic, additional CJK coverage
• Multi-column prospectus layouts
• Table-of-contents anchor refinement
• Full audit pass on parser.py to tighten ruff rules

Origin

Cartographer was extracted from Atlas, a personal financial data platform. It exists as a standalone library because the pipeline has utility beyond any single consumer — anyone processing annual reports or regulatory filings at scale will benefit.

License

MIT. See LICENSE.

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Built by Hugo Condesa.