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PaddleOCR Text Recognition

Free

Extract text from images and PDFs with high accuracy.

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Free · Opens the source repo

What PaddleOCR Text Recognition does

PaddleOCR Text Recognition is a command-line skill designed for extracting machine-readable text from various image formats, including screenshots, photos, and scanned documents. This skill is particularly useful for developers and designers who need to automate the extraction of textual information from visual content. It supports line-level text extraction and can return bounding box coordinates for each recognized text segment, making it ideal for applications that require precise text positioning.

The skill is built on PaddleOCR, a robust optical character recognition (OCR) framework known for its strong performance, especially with CJK (Chinese, Japanese, Korean) characters, small print, and even handwritten text. By utilizing this skill, users can seamlessly convert images or PDFs into plain text, which can then be processed further or integrated into other applications. The command-line interface allows for easy integration into scripts or workflows, enabling batch processing of documents.

Users can extract text from both local files and URLs, making it versatile for different use cases. The skill also offers preprocessing options to enhance accuracy, especially for images with distortion or unclear orientations. For well-scanned documents, users can disable preprocessing to speed up the extraction process. This flexibility allows users to tailor the OCR process according to the quality and type of input they are working with.

PaddleOCR Text Recognition is suitable for anyone needing reliable text extraction capabilities, whether for data entry, content digitization, or enhancing accessibility. Its straightforward command-line usage makes it accessible for both technical and non-technical users, while its accuracy ensures that the extracted text is usable in various applications.

When to use it

Use this skill when you need to extract text from images, screenshots, or scanned PDFs for further processing or analysis.

When not to use it

Avoid using this skill for documents with complex layouts, such as those containing tables or charts, as it is not designed for document parsing.

What you can build with it

Extracting Text from Scanned Documents

Use this skill to automate the extraction of text from scanned documents for data entry or archival purposes.

Processing Screenshots for Text Analysis

Quickly extract text from screenshots taken during research or meetings to facilitate documentation and analysis.

Digitizing Handwritten Notes

Utilize the skill to convert handwritten notes captured in images into machine-readable text for easier editing and sharing.

How to install PaddleOCR Text Recognition

View source

1. Install with the skills CLI

npx skills add paddlepaddle/paddleocr/paddleocr-text-recognition --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by paddlepaddle

PaddleOCR Text Recognition

When to Use This Skill

Use this skill for:

  • Extract text from images (screenshots, photos, scans)
  • Extract text from PDFs or document images when the goal is line/box-level text
  • Extract text from URLs or local files that point to images/PDFs

Do not use for:

  • Documents with tables, formulas, charts, or complex layouts — use Document Parsing instead

Usage

Basic OCR

From URL:

paddleocr api \
  --model_type ocr \
  --file_url "https://example.com/image.png"

From local file:

paddleocr api \
  --model_type ocr \
  --file_path "./document.pdf"

Common Options

# With specific model
paddleocr api \
  --model_type ocr \
  --model PP-OCRv5 \
  --file_path "./report.pdf"

# Disable preprocessing (faster, for flat/well-oriented images)
paddleocr api \
  --model_type ocr \
  --file_path "./document.pdf" \
  --use_doc_unwarping False \
  --use_doc_orientation_classify False

# Save result to file
paddleocr api \
  --model_type ocr \
  --file_url "https://..." \
  --output result.json

# Page ranges
paddleocr api \
  --model_type ocr \
  --file_path "./large.pdf" \
  --page_ranges "1-5,10,15-20"

Output Format

{
  "jobId": "job-xxx",
  "pages": [
    {
      "prunedResult": {
        "rec_texts": ["Line 1", "Line 2"],
        "rec_scores": [0.98, 0.95]
      },
      "ocrImageUrl": "https://..."
    }
  ]
}

Important Notes

Preprocessing options: By default, the API enables document preprocessing (unwarping and orientation classification). For flat, well-oriented images (screenshots, properly scanned documents), you can disable preprocessing for faster results:

paddleocr api --model_type ocr --file_path "./document.pdf" --use_doc_unwarping False --use_doc_orientation_classify False

Keep preprocessing enabled when:

  • The input is a photo of a curved or folded document
  • The document has significant perspective distortion
  • Orientation is uncertain (rotated 90/180/270 degrees)

Display complete results: Always show the full extracted content to users. Do not truncate with "..." unless content exceeds 10,000 characters. When multiple pages are processed, summarize if needed but provide complete results when explicitly requested.

Handle errors gracefully: When the CLI returns an error, inform the user of the specific issue rather than silently failing or falling back to your own vision capabilities. Common errors:

  • Authentication: PADDLEOCR_ACCESS_TOKEN invalid or missing
  • Quota: API rate limit exceeded
  • No content detected: Image may be blank or contain no text

CLI Reference

Run paddleocr api --help for all options.

For full documentation, see: PaddleOCR Official Documentation

Frequently asked questions about PaddleOCR Text Recognition

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