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myxlyx/
cn-financial-report-inspector

Name: cn-financial-report-inspector
Availability: InStock

pdf-processing

chinese-financial-reports

annual-reports

pymupdf

research

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cn-financial-report-inspector

cn-financial-report-inspector is a research project for inspecting Chinese listed-company annual reports. The long-term direction is an Adaptive RAG / Agent system, but this stage intentionally focuses only on the PDF processing module.

Current Scope

The MVP converts text-based Chinese financial report PDFs into clean intermediate files for later table checking, RAG indexing, and synthetic error construction.

This stage does not implement:

RAG
Agent workflows
Law or regulation knowledge graphs
Financial error detection
OCR

Scanned PDFs or badly parsed PDFs are intentionally detected and excluded in stage 1.

Where To Put PDFs

Place source PDFs under:

data/raw_pdfs/

PDF filenames may contain Chinese characters. The parser does not hard-code any report names.

Install Dependencies

pip install -e .

The MVP uses lightweight dependencies:

PyMuPDF
pandas
tqdm

Run Parsing

From the project root:

python scripts/parse_pdfs.py

The script will:

Detect each PDF type.
Write one manifest JSON per PDF.
Skip PDFs that are not classified as text_based.
Extract page-level text from text-based PDFs.
Export Markdown, JSONL pages, table CSV/JSON files, and report metadata.
Print a concise terminal summary.

Output Structure

text

data/
  manifests/
    <report_id>.json
  parsed_reports/
    <report_id>/
      report.md
      pages.jsonl
      metadata.json
      tables/
        table_001.csv
        table_001.json

Manifest example:

json

{
  "report_id": "...",
  "source_pdf": "...",
  "pdf_type": "text_based",
  "page_count": 0,
  "text_pages": 0,
  "avg_text_chars_per_page": 0,
  "should_parse": true,
  "notes": []
}

Page JSONL line example:

{"page": 1, "text": "...", "char_count": 1234}

Report metadata example:

json

{
  "report_id": "...",
  "source_pdf": "...",
  "pdf_type": "text_based",
  "page_count": 0,
  "markdown_path": "report.md",
  "pages_jsonl_path": "pages.jsonl",
  "tables_count": 0,
  "tables_dir": "tables",
  "parse_warnings": []
}

PDF Type Detection

The first version uses PyMuPDF to inspect:

page count
pages with extractable text
average text characters per page
whether most pages have selectable text

PDFs are classified as:

text_based
mixed
scanned_or_image_based
parse_failed

Only text_based PDFs are fully parsed.

Current Limitations

No OCR is performed.
Scanned and image-based PDFs are skipped by design.
Table extraction uses PyMuPDF page.find_tables() on a best-effort basis.
Table extraction quality depends on the PDF layout and installed PyMuPDF version.
Extracted text is page-level plain text, not a semantic document hierarchy.

myxlyx/cn-financial-report-inspector