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

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.
Execute document parsing:
```
python scripts/vl_caller.py --file-url "URL provided by user" --pretty
```
Or for local files:
```
python scripts/vl_caller.py --file-path "file path" --pretty
```
Optional: explicitly set file type:
```
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: <system-temp>/paddleocr/doc-parsing/results/result_<timestamp>_<id>.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
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
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:

{
  "ok": false,
  "text": "",
  "result": null,
  "error": {
    "code": "CONFIG_ERROR",
    "message": "PADDLEOCR_DOC_PARSING_API_URL not configured. Get your API at: https://paddleocr.com"
  }
}

Configuration workflow:

Show the exact error message to the user.
Guide the user to obtain credentials: Visit the PaddleOCR website, click API, select a model (PP-StructureV3, PaddleOCR-VL, or PaddleOCR-VL-1.5), then copy the API_URL and Token. They map to these environment variables:
- PADDLEOCR_DOC_PARSING_API_URL — full endpoint URL ending with /layout-parsing
- PADDLEOCR_ACCESS_TOKEN — 40-character alphanumeric string
Optionally configure PADDLEOCR_DOC_PARSING_TIMEOUT for request timeout. Recommend using the host application's standard configuration method rather than pasting credentials in chat.
Apply credentials — one of:
- User configured via the host UI: ask the user to confirm, then retry.
- User pastes credentials in chat: warn that they may be stored in conversation history, help the user persist them using the host's standard configuration method, then retry.

Handling Large Files

For PDFs, the maximum is 100 pages per request.

Optimize Large Images Before Parsing

For large image files, compress before uploading — this reduces upload time and can improve processing stability:

python scripts/optimize_file.py input.png output.jpg --quality 85
python scripts/vl_caller.py --file-path "output.jpg" --pretty

--quality controls JPEG/WebP lossy compression (1-100, default 85); it has no effect on PNG output. Use --target-size (in MB, default 20) to set the max file size — the script iteratively downscales until the target is met.

Requires optional dependencies: pip install -r requirements-optimize.txt

Use URL for Large Local Files (Recommended)

For very large local files, prefer --file-url over --file-path to avoid base64 encoding overhead:

python scripts/vl_caller.py --file-url "https://your-server.com/large_file.pdf"

Process Specific Pages (PDF Only)

If you only need certain pages from a large PDF, extract them first:

# Extract pages 1-5
python scripts/split_pdf.py large.pdf pages_1_5.pdf --pages "1-5"

# Mixed ranges are supported
python scripts/split_pdf.py large.pdf selected_pages.pdf --pages "1-5,8,10-12"

# Then process the smaller file
python scripts/vl_caller.py --file-path "pages_1_5.pdf"

Error Handling

All errors return JSON with ok: false. Show the error message and stop — do not fall back to your own vision capabilities. Identify the issue from error.code and error.message:

Authentication failed (403) — error.message contains "Authentication failed"

Token is invalid, reconfigure with correct credentials

Quota exceeded (429) — error.message contains "API rate limit exceeded"

Daily API quota exhausted, inform user to wait or upgrade

Unsupported format — error.message contains "Unsupported file format"

File format not supported, convert to PDF/PNG/JPG

No content detected:

text field is empty
Document may be blank, image-only, or contain no extractable text

Tips for Better Results

If parsing quality is poor:

Large or high-resolution images: Compress with optimize_file.py before parsing — oversized inputs can degrade layout detection:
```
python scripts/optimize_file.py input.png optimized.jpg --quality 85
```
Check confidence: result.result.layoutParsingResults[n].prunedResult includes confidence scores per layout element — low values indicate regions worth reviewing

Reference Documentation

references/output_schema.md — Full output schema, field descriptions, and command examples

Note: Model version and capabilities are determined by your API endpoint (PADDLEOCR_DOC_PARSING_API_URL).

Testing the Skill

To verify the skill is working properly:

python scripts/smoke_test.py
python scripts/smoke_test.py --skip-api-test
python scripts/smoke_test.py --test-url "https://..."

The first form tests configuration and API connectivity. --skip-api-test checks configuration only. --test-url overrides the default sample document URL.