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

1. Identify the input source:

- User provides URL: Use the --file-url parameter - User provides local file path: Use the --file-path parameter - User uploads image: Save it first, then use --file-path

1. Execute OCR:

``bash python scripts/ocrcaller.py --file-url "URL provided by user" --pretty ` Or for local files: `bash python scripts/ocrcaller.py --file-path "file path" --pretty ``

1. Parse JSON response:

- Check the ok field: true means success, false means error - Extract text: result.fulltext contains all recognized text - Get quality: quality.qualityscore indicates recognition confidence (0.0-1.0) - Handle errors: If ok is false, display error.message

1. 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:

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 user
• quality.quality_score: 0.72+ is good, <0.5 is poor
• error.message: If ok is 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 endpoint
• PADDLEOCRTOKEN: 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:

If quality is below 0.5, mention to the user and suggest:

• Try using --mode quality for 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 scoring
• references/normalized_schema.md - Complete output schema specification
• references/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.