--- name: paddleocr-doc-parsing description: >- Use this skill to extract structured Markdown/JSON from PDFs and document images—tables with cell-level precision, formulas as LaTeX, figures, seals, charts, headers/footers, multi-column layout and correct reading order. Trigger terms: 文档解析, 版面分析, 版面还原, 表格提取, 公式识别, 多栏排版, 扫描件结构化, 发票, 财报, 复杂 PDF, PDF转Markdown, 图表, 阅读顺序; reading order, formula, LaTeX, layout parsing, structure extraction, PP-StructureV3, PaddleOCR-VL. metadata: openclaw: requires: env: - PADDLEOCR_DOC_PARSING_API_URL - PADDLEOCR_ACCESS_TOKEN bins: - python primaryEnv: PADDLEOCR_ACCESS_TOKEN emoji: "📄" homepage: https://github.com/PaddlePaddle/PaddleOCR/tree/main/skills/paddleocr-doc-parsing ---

PaddleOCR Document Parsing Skill

When to Use This Skill

Trigger keywords (routing): Bilingual trigger terms (Chinese and English) are listed in the YAML description above—use that field for discovery and routing.

Use this skill for:

• Documents with tables (invoices, financial reports, spreadsheets)
• Documents with mathematical formulas (academic papers, scientific documents)
• Documents with charts and diagrams
• Multi-column layouts (newspapers, magazines, brochures)
• Complex document structures requiring layout analysis
• Any document requiring structured understanding

Do not use for:

• Simple text-only extraction
• Quick OCR tasks where speed is critical
• Screenshots or simple images with clear text

Installation

Install Python dependencies before using this skill. From the skill directory (skills/paddleocr-doc-parsing):

pip install -r requirements.txt

Optional — for image optimization and PDF page extraction:

pip install -r requirements-optimize.txt

How to Use This Skill

> Working directory: All python scripts/... commands below should be run from this skill's root directory (the directory containing this SKILL.md file).

Basic Workflow

1. Identify the input source:

- User provides URL: Use the --file-url parameter - User provides local file path: Use the --file-path parameter

Input type note: - Supported file types depend on the model and endpoint configuration. - Always follow the file type constraints documented by your endpoint API.

1. Execute document parsing:

```bash python scripts/vl_caller.py --file-url "URL provided by user" --pretty ```

Or for local files:

```bash python scripts/vl_caller.py --file-path "file path" --pretty ```

Optional: explicitly set file type:

```bash python scripts/vl_caller.py --file-url "URL provided by user" --file-type 0 --pretty ```

- --file-type 0: PDF - --file-type 1: image - If omitted, the type is auto-detected from the file extension. For local files, a recognized extension (.pdf, .png, .jpg, .jpeg, .bmp, .tiff, .tif, .webp) is required; otherwise pass --file-type explicitly. For URLs with unrecognized extensions, the service attempts inference.

> Performance note: Parsing time scales with document complexity. Single-page images typically complete in 1-5 seconds; large PDFs (50+ pages) may take several minutes. Allow adequate time before assuming a timeout.

Default behavior: save raw JSON to a temp file: - If --output is omitted, the script saves automatically under the system temp directory - Default path pattern: /paddleocr/doc-parsing/results/result__.json - If --output is provided, it overrides the default temp-file destination - If --stdout is provided, JSON is printed to stdout and no file is saved - In save mode, the script prints the absolute saved path on stderr: Result saved to: /absolute/path/... - In default/custom save mode, read and parse the saved JSON file before responding - Use --stdout only when you explicitly want to skip file persistence

1. Parse JSON response:

- Check the ok field: true means success, false means error - The output contains complete document data: text, tables, formulas (LaTeX), figures, seals, headers/footers, and reading order - Use the appropriate field based on what the user needs: - text — full document text across all pages - result.result.layoutParsingResults[n].markdown.text — page-level markdown - result.result.layoutParsingResults[n].prunedResult — structured layout data with positions and confidence - Handle errors: If ok is false, display error.message

1. Present results to user:

- Display content based on what the user requested (see "Complete Output Display" below) - If the content is empty, the document may contain no extractable text - In save mode, always tell the user the saved file path and that full raw JSON is available there

What to Do After Parsing

Common next steps once you have the structured output:

• Save as Markdown: Write the text field to a .md file — tables, headings, and formulas are preserved
• Extract specific tables: Navigate result.result.layoutParsingResults[n].prunedResult to access individual layout elements with position and confidence data
• Feed to RAG / search pipeline: The text field is structured markdown, ready for chunking and indexing
• Poor results: See "Tips for Better Results" below before retrying

Complete Output Display

Display the COMPLETE extracted content based on what the user asked for. The parsed output is only useful if the user receives all of it — truncation silently drops data.

• If user asks for "all text", show the entire text field
• If user asks for "tables", show ALL tables in the document
• If user asks for "main content", filter out headers/footers but show ALL body text
• Do not truncate with "..." unless content is excessively long (>10,000 chars)
• Do not say "Here's a preview" when user expects complete output

Example - Correct:

User: "Extract all the text from this document"
Agent: I've parsed the complete document. Here's all the extracted text:

[Display entire text field or concatenated regions in reading order]

Document Statistics:
- Total regions: 25
- Text blocks: 15
- Tables: 3
- Formulas: 2
Quality: Excellent (confidence: 0.92)

Example - Incorrect:

User: "Extract all the text"
Agent: "I found a document with multiple sections. Here's the beginning:
'Introduction...' (content truncated for brevity)"

Understanding the Output

The script returns an envelope with ok, text, result, and error. Use text for the full document content; navigate result.result.layoutParsingResults[n] for per-page structured data.

For the complete schema and field-level details, see references/output_schema.md.

> Raw result location (default): the temp-file path printed by the script on stderr

Usage Examples

Example 1: Extract Full Document Text

python scripts/vl_caller.py \
  --file-url "https://example.com/paper.pdf" \
  --pretty

Then use:

• Top-level text for quick full-text output
• result.result.layoutParsingResults[n].markdown when page-level output is needed

Example 2: Extract Structured Page Data

python scripts/vl_caller.py \
  --file-path "./financial_report.pdf" \
  --pretty

Then use:

• result.result.layoutParsingResults[n].prunedResult for structured parsing data (layout/content/confidence)

Example 3: Print JSON to stdout (without saving to file)

python scripts/vl_caller.py \
  --file-url "URL" \
  --stdout \
  --pretty

By default the script writes JSON to a temp file and prints the path to stderr. Add --stdout to print the full JSON directly to stdout instead. Use this when you need to inspect the result inline or pipe it to another tool.

First-Time Configuration

When API is not configured, the script outputs:

...