agentleFS
weininghuiweininghui/skills/paddleocr-doc-parsing/SKILL.md

paddleocr-doc-parsing

Skill1 starsChanged 13 days agoMITView on GitHub

Parse documents using PaddleOCR's API. Supports both sync and async modes for images and PDFs.

  • Reads credentials

5 other projects use a version of this.

Adopted by

Most recently taken up by

The file

Files
SKILL.md

运行依赖:需要本机已安装命令 base64、curl、jq、python3;需要环境变量 PADDLEOCR_ACCESS_TOKEN、PADDLEOCR_API_URL(未配置则该技能不可用);缺失时先安装 / 配置,再执行正文步骤。

PaddleOCR Document Parsing

Parse images and PDF files using PaddleOCR's API. Supports both synchronous and asynchronous parsing modes with structured output.

Resource Links

Key Features

  • Multi-format support: PDF and image files (JPG, PNG, BMP, TIFF)
  • Two parsing modes:
    • Sync mode: Fast response for small files (<600s timeout)
    • Async mode: For large files with progress polling
  • Layout analysis: Automatic detection of text blocks, tables, formulas
  • Multi-language: Support for 110+ languages
  • Structured output: Markdown format with preserved document structure

Setup

  1. Visit PaddleOCR to obtain your API credentials
  2. Set environment variables:
export PADDLEOCR_ACCESS_TOKEN="your_token_here"
export PADDLEOCR_API_URL="https://your-endpoint.aistudio-app.com/layout-parsing"

# Optional: For async mode
export PADDLEOCR_JOB_URL="https://your-job-endpoint.aistudio-app.com/api/v2/ocr/jobs"
export PADDLEOCR_MODEL="PaddleOCR-VL-1.5"

Usage Examples

Sync Mode (Default)

For small files and quick processing:

# Parse local image
{baseDir}/paddleocr_parse.sh document.jpg

# Parse PDF
{baseDir}/paddleocr_parse.sh -t pdf document.pdf

# Parse from URL
{baseDir}/paddleocr_parse.sh https://example.com/document.jpg

# Save output to file
{baseDir}/paddleocr_parse.sh -o result.json document.jpg

# Verbose output
{baseDir}/paddleocr_parse.sh -v document.jpg

Async Mode

For large files with progress tracking:

# Parse large PDF with async mode
{baseDir}/paddleocr_parse.sh --async large-document.pdf

# Parse from URL with async mode
{baseDir}/paddleocr_parse.sh --async -t pdf https://example.com/doc.pdf

# Save async result to file
{baseDir}/paddleocr_parse.sh --async -o result.json document.pdf

Using Python Script Directly

# Sync mode
python3 {baseDir}/paddleocr_parse.py document.jpg

# Async mode
python3 {baseDir}/paddleocr_parse.py --async-mode document.pdf

# With output file
python3 {baseDir}/paddleocr_parse.py -o result.json --async-mode document.pdf

Response Structure

{
  "logId": "unique_request_id",
  "errorCode": 0,
  "errorMsg": "Success",
  "result": {
    "layoutParsingResults": [
      {
        "prunedResult": [...],
        "markdown": {
          "text": "# Document Title\n\nParagraph content...",
          "images": {}
        },
        "outputImages": [...],
        "inputImage": "http://input-image"
      }
    ],
    "dataInfo": {...}
  }
}

Important Fields:

  • prunedResult - Contains detailed layout element information including positions, categories, etc.
  • markdown - Stores the document content converted to Markdown format with preserved structure and formatting.

Mode Selection Guide

Use CaseRecommended Mode
Small images (< 10MB)Sync
Single page PDFsSync
Large PDFs (> 10MB)Async
Multi-page documentsAsync
Batch processingAsync
Quick text extractionSync

Error Handling

The script will exit with code 1 and print error message for:

  • Missing required environment variables
  • File not found
  • API authentication failures
  • Invalid JSON responses
  • API error codes (non-zero)

Quota Information

See official documentation: https://ai.baidu.com/ai-doc/AISTUDIO/Xmjclapam

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.