read-ocr

name	read-ocr
description	Use this skill when users need to extract text from images, PDFs, or documents. Supports URLs and local files, with adaptive quality modes. Returns structured JSON containing recognized text, confidence scores, and quality metrics.

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

2. Execute OCR:

   python scripts/ocr_caller.py --file-url "URL provided by user" --pretty
   

   Or for local files:

   python scripts/ocr_caller.py --file-path "file path" --pretty
   

3. Parse JSON response:

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

4. 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 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
• PADDLE_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 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.