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name: pdf-vision description: Extract text content from image-based/scanned PDFs using multiple vision APIs with automatic fallback. Supports Xflow (qwen3-vl-plus) and ZhipuAI (GLM-4.6V-Flash, GLM-5) vision models. This skill converts PDF pages to images and uses AI vision capabilities to extract structured text, tables, and content from scanned documents that cannot be processed with traditional text extraction methods. license: MIT

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PDF Vision Extraction Skill (Enhanced)

Overview

This skill handles image-based or scanned PDFs that contain no selectable text. It supports multiple vision APIs with automatic fallback:

Primary Models

• Xflow: qwen3-vl-plus (your primary vision model)
• ZhipuAI: glm-4.6v-flash (free vision model with fallback support)
• Fallback: glm-5 (text-only, but may work with some image prompts)

Unlike traditional PDF text extraction tools (pdftotext, pdfplumber) which only work on text-based PDFs, this skill can process: - Scanned documents - Image-only PDFs

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- Photographed documents - Handwritten notes (with limitations) - Complex layouts with tables and formatting

Supported Models

Vision-Capable Models

Provider	Model	Type	Context	Free
Xflow	qwen3-vl-plus	Vision + Text	131K	❌
ZhipuAI	glm-4.6v-flash	Vision + Text	32K	✅
ZhipuAI	glm-5	Text-only*	128K	❌

Additional Text Models (for fallback)

Provider	Model	Context	Free
ZhipuAI	glm-4-flash-250414	128K	✅
ZhipuAI	cogview-3-flash	32K	✅

*Note: glm-5 is primarily text-only but may handle image prompts in some cases.

Prerequisites

1. API Configuration

Your OpenClaw must be configured with both providers:

Xflow Configuration (already set up): - models.providers.openai.baseUrl: https://apis.iflow.cn/v1 - models.providers.openai.apiKey: Your Xflow API key

ZhipuAI Configuration (update token): - models.providers.zhipuai.baseUrl: https://open.bigmodel.cn/api/paas/v4 - models.providers.zhipuai.apiKey: Your ZhipuAI API token

2. Required System Tools

• pypdfium2 Python library (for PDF to image conversion)
• curl (for API calls)
• base64 (for image encoding)

3. Python Libraries (already installed)

pypdfium2

Usage

Automatic Fallback Mode (Default)

Uses Xflow first, falls back to ZhipuAI if needed:

./scripts/pdf_vision.py --pdf-path /path/to/document.pdf

Specific Model Selection

Force a specific model for cost or performance reasons:

# Use free GLM-4.6V-Flash model
./scripts/pdf_vision.py --pdf-path document.pdf --model zhipuai/glm-4.6v-flash

# Use specific Xflow model  
./scripts/pdf_vision.py --pdf-path document.pdf --model openai/qwen3-vl-plus

# Short form (auto-detects provider)
./scripts/pdf_vision.py --pdf-path document.pdf --model glm-4.6v-flash

Structured Data Extraction

./scripts/pdf_vision.py --pdf-path invoice.pdf --prompt "Extract as JSON: vendor, date, total" --model glm-4.6v-flash

Multi-page PDF Handling

# Process page 3 specifically
./scripts/pdf_vision.py --pdf-path book.pdf --page 3 --output page3.txt

Configuration

Environment Variables

The skill reads configuration from your OpenClaw config file (~/.openclaw/openclaw.json): - models.providers.openai.baseUrl & apiKey - models.providers.zhipuai.baseUrl & apiKey

Output Format

Returns extracted text content as a string. For structured data requests, the AI model will format output according to your prompt instructions.

Examples

Cost-Optimized Extraction (Free Model)

Command: --model glm-4.6v-flash Use case: When you want to use free vision capabilities Result: Good quality extraction at no cost

High-Quality Extraction (Premium Model)

Command: --model qwen3-vl-plus Use case: When you need maximum accuracy and complex layout understanding Result: Best possible extraction quality

Automatic Fallback (Recommended)

Command: No --model flag Use case: Production environments where reliability is key Result: Uses best available model, falls back gracefully

Model Comparison

GLM-4.6V-Flash (Free)

• ✅ Completely free
• ✅ Good Chinese text recognition
• ✅ Decent table structure preservation
• ⚠️ Lower context window (32K vs 131K)
• ⚠️ May struggle with very complex layouts

Qwen3-VL-Plus (Premium)

• ✅ Superior image understanding
• ✅ Excellent table and structure recognition
• ✅ Larger context window (131K)
• ✅ Better handling of mixed languages
• ❌ Requires paid API access

Limitations

• Single page processing: Currently processes one page at a time
• Image quality: Better results with higher resolution scans
• Complex layouts: May struggle with very dense or overlapping text
• Handwriting: Limited accuracy with handwritten content
• File size: Large PDFs may exceed API token limits

Technical Implementation

The skill follows this workflow: 1. PDF to Image: Converts specified PDF page to PNG using pypdfium2 2. Model Selection: Chooses model based on user preference or fallback logic 3. API Call: Sends image + prompt to selected vision API endpoint 4. Response Parsing: Extracts and returns the AI-generated text content 5. Fallback: If primary model fails, tries alternative models

For debugging, temporary files are created in /tmp/: - /tmp/pdf_vision_page.png - converted image - /tmp/pdf_vision_payload_*.json - API request payload
- /tmp/pdf_vision_response_*.json - API response

Integration Notes

This skill complements the standard pdf skill: - Use pdf skill for text-based PDFs (faster, no API cost) - Use pdf-vision skill for image-based/scanned PDFs (requires vision API)

Both skills can be used together in a fallback pattern: 1. Try pdf skill first 2. If no text extracted, fall back to pdf-vision skill

Cost Optimization Tips

1. Use GLM-4.6V-Flash for routine tasks - it's free and quite capable
2. Reserve Qwen3-VL-Plus for complex documents - when you need maximum accuracy
3. Test both models on your document types - choose based on your quality requirements
4. Monitor API usage - track which models you're using most

Update Your GLM API Token

Replace the placeholder token in your config:

# Replace YOUR_ACTUAL_GLM_TOKEN with your real token
sed -i 's/YOUR_GLM_API_TOKEN_HERE/YOUR_ACTUAL_GLM_TOKEN/g' ~/.openclaw/openclaw.json

这是一款用于从扫描PDF提取文字的工具，支持多个AI模型并能自动切换使用。文档写得非常详细清楚，配置使用不复杂，中文识别效果不错，适合处理扫描文档和图片型PDF。不过目前只能逐页处理，多页文档需要多次操作，对复杂排版的识别效果可能有限。整体质量可靠，作为特定场景的辅助工具比较实用。