PP-OCRv5 API Skill
When to Use This Skill
Invoke this skill in the following situations:
- Extract text from images (screenshots, photos, scans, charts)
- Read text from PDF or document images
- Perform OCR on any visual content containing text
- Parse structured documents (invoices, receipts, forms, tables)
- Recognize text in photos taken by mobile phones
- Extract text from URLs pointing to images or PDFs
Do not use this skill in the following situations:
- Plain text files that can be read directly with the Read tool
- Code files or markdown documents
- Tasks that do not involve image-to-text conversion
How to Use This Skill
Basic Workflow
-
Identify the input source:
- User provides URL: Use the
--file-urlparameter - User provides local file path: Use the
--file-pathparameter - User uploads image: Save it first, then use
--file-path
- User provides URL: Use the
-
Execute OCR:
python scripts/ocr_caller.py --file-url "URL provided by user" --prettyOr for local files:
python scripts/ocr_caller.py --file-path "file path" --pretty -
Parse JSON response:
- Check the
okfield:truemeans success,falsemeans error - Extract text:
result.full_textcontains all recognized text - Get quality:
quality.quality_scoreindicates recognition confidence (0.0-1.0) - Handle errors: If
okis false, displayerror.message
- Check the
-
Present results to user:
- Display extracted text in a readable format
- If quality score is low (<0.5), alert the user
- If structured output is needed, use
result.pages[].items[]to get line-by-line data
Mode Selection
Always use --mode auto (default) unless the user explicitly requests otherwise:
| User Request | Use Mode | Command Flag |
|--------------|----------|--------------|
| Default/unspecified | Auto (adaptive) | --mode auto (or omit) |
| "Quick recognition" / "fast" | Fast | --mode fast |
| "High precision" / "accurate" | Quality | --mode quality |
Auto mode (recommended): Automatically tries 1-3 times, progressively increasing correction levels, returning the best result.
Usage Mode Examples
Mode 1: Simple URL OCR
python scripts/ocr_caller.py --file-url "https://example.com/invoice.jpg" --pretty
Mode 2: Local File OCR
python scripts/ocr_caller.py --file-path "./document.pdf" --pretty
Mode 3: Fast Mode for Clear Images
python scripts/ocr_caller.py --file-url "URL" --mode fast --pretty
Understanding the Output
The script outputs JSON structure as follows:
{
"ok": true,
"result": {
"full_text": "All recognized text here...",
"pages": [...]
},
"quality": {
"quality_score": 0.85,
"text_items": 42
}
}
Key fields to extract:
result.full_text: Complete text for the userquality.quality_score: 0.72+ is good, <0.5 is poorerror.message: Ifokis false, provides error description
First-Time Configuration
If the user has not configured API credentials, run:
python scripts/configure.py
This will prompt for:
API_URL: Paddle AI Studio endpointPADDLE_OCR_TOKEN: User's access token
Configuration is saved to the .env file, only needs to be configured once.
Error Handling
Configuration missing:
Error: API_URL not configured
→ Run python scripts/configure.py
Authentication failed (403):
error_code: PROVIDER_AUTH_ERROR
→ Token is invalid, reconfigure with correct credentials
Quota exceeded (429):
error_code: PROVIDER_QUOTA_EXCEEDED
→ Daily API quota exhausted, inform user to wait or upgrade
No text detected:
quality_score: 0.0, text_items: 0
→ Image may be blank, corrupted, or contain no text
Quality Interpretation
When presenting results to users, consider the quality score:
| Quality Score | Explanation to User | |---------------|---------------------| | 0.90 - 1.00 | Excellent recognition quality | | 0.72 - 0.89 | Good recognition quality (default target) | | 0.50 - 0.71 | Fair recognition quality, may have some errors | | 0.00 - 0.49 | Poor recognition quality or no text detected |
If quality is below 0.5, mention to the user and suggest:
- Try using
--mode qualityfor better accuracy - Check if the image is clear and contains text
- Provide a higher resolution image if possible
Advanced Options
Use only when explicitly requested by the user:
Include raw provider response (for debugging):
python scripts/ocr_caller.py --file-url "URL" --return-raw-provider
Request visualization (show detection regions):
python scripts/ocr_caller.py --file-url "URL" --visualize
Adjust auto mode parameters:
python scripts/ocr_caller.py --file-url "URL" \
--max-attempts 2 \
--quality-target 0.80 \
--budget-ms 20000
Reference Documentation
For in-depth understanding of the OCR system, refer to:
references/agent_policy.md- Auto mode strategy and quality scoringreferences/normalized_schema.md- Complete output schema specificationreferences/provider_api.md- Provider API contract details
Load these reference documents into context when:
- Debugging complex issues
- User asks about quality scoring algorithm
- Need to understand adaptive retry mechanism
- Customizing auto mode parameters
Testing the Skill
To verify the skill is working properly:
python scripts/smoke_test.py
This tests configuration and API connectivity.