PDF OCR Extraction – OpenClaw Skill

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name: PDF OCR Extraction description: Extract text from scanned PDFs using optical character recognition author: claude-office-skills version: "1.0" tags: [pdf, ocr, text-extraction, scanning, document] models: [claude-sonnet-4, claude-opus-4] tools: [computer, file_operations]

PDF OCR Extraction

Extract text from scanned documents and image-based PDFs using OCR technology.

Overview

This skill helps you:

• Extract text from scanned documents
• Make image PDFs searchable
• Digitize paper documents
• Process handwritten text (limited)
• Batch process multiple documents

How to Use

Basic OCR

"Extract text from this scanned PDF"
"OCR this document image"
"Make this PDF searchable"

With Options

"Extract text from pages 1-10, English language"
"OCR this document, preserve layout"
"Extract and output as structured data"

Document Types

OCR Quality by Document Type

Document Type	Expected Quality	Tips
Typed documents	⭐⭐⭐⭐⭐ 95%+	Best results
Printed books	⭐⭐⭐⭐ 90%+	Watch for aging
Forms	⭐⭐⭐⭐ 85%+	Check boxes may need manual
Tables/Data	⭐⭐⭐ 80%+	Structure may need fixing
Handwritten (neat)	⭐⭐ 60-80%	Variable results
Handwritten (cursive)	⭐ 30-60%	Often needs manual review
Mixed content	⭐⭐⭐ 75%+	Depends on complexity

Output Formats

Plain Text Extraction

## OCR Result: [Document Name]

**Pages Processed**: [X]
**Language**: [Detected/Specified]
**Confidence**: [X]%

---

[Extracted text content here]

---

### Notes
- [Any issues or uncertainties]
- [Characters that may be incorrect]

Structured Extraction

## OCR Extraction: [Document Name]

### Document Info
| Field | Value |
|-------|-------|
| Title | [Extracted or inferred] |
| Date | [If found] |
| Author | [If found] |

### Content by Section

#### [Header 1]
[Content under this header]

#### [Header 2]
[Content under this header]

### Tables Found
| Column 1 | Column 2 | Column 3 |
|----------|----------|----------|
| [Data] | [Data] | [Data] |

### Uncertain Text
| Page | Original | Confidence | Possible |
|------|----------|------------|----------|
| 3 | "teh" | 70% | "the" |
| 5 | "l0ve" | 65% | "love" |

Searchable PDF Output

## OCR to Searchable PDF

**Source**: [filename.pdf]
**Output**: [filename_searchable.pdf]

### Processing Summary
| Metric | Value |
|--------|-------|
| Pages | [X] |
| Words extracted | [Y] |
| Average confidence | [Z]% |
| Processing time | [T] seconds |

### Quality Report
- [X] pages with 95%+ confidence
- [Y] pages with 80-94% confidence
- [Z] pages with <80% confidence (review recommended)

### Searchability
✅ Document is now text-searchable
✅ Original images preserved
✅ Text layer added behind images

Pre-Processing Tips

Image Quality Checklist

Before OCR, ensure:

• Resolution: 300 DPI minimum (600 for small text)
• Contrast: Clear black text on white background
• Alignment: Document is straight (not skewed)
• Completeness: No cut-off edges
• Cleanliness: No stains, marks, or shadows

Common Pre-Processing Steps

Issue	Solution
Low resolution	Upscale image first
Skewed/rotated	Auto-deskew
Poor contrast	Adjust levels/threshold
Noise/specks	Apply noise reduction
Shadows	Flatten lighting
Color document	Convert to grayscale

Language Support

Supported Languages

• Excellent: English, Spanish, French, German, Italian
• Good: Chinese (Simplified/Traditional), Japanese, Korean
• Moderate: Arabic, Hebrew (RTL support), Hindi
• Basic: Many others with varying quality

Multi-Language Documents

"OCR this document, detect language automatically"
"Extract text, primary: English, secondary: Chinese"

Handling Specific Content

Forms and Checkboxes

## Form Extraction: [Form Name]

### Field Values
| Field | Value | Confidence |
|-------|-------|------------|
| Name | John Smith | 98% |
| Date | 01/15/2026 | 95% |
| Address | 123 Main St | 92% |

### Checkboxes
| Question | Checked |
|----------|---------|
| Option A | ☑️ Yes |
| Option B | ☐ No |
| Option C | ☑️ Yes |

### Signature
[Signature detected on page X - cannot extract text]

Tables

## Table Extraction

### Table 1 (Page 2)
| Header A | Header B | Header C |
|----------|----------|----------|
| Value 1 | Value 2 | Value 3 |
| Value 4 | Value 5 | Value 6 |

**Table confidence**: 85%
**Note**: Column 3 may have alignment issues

Handwritten Text

## Handwritten Text Extraction

**Legibility Assessment**: [Good/Fair/Poor]
**Recommended**: Manual review

### Extracted Text (Confidence: 65%)
[Extracted text with uncertain words marked]

### Uncertain Words
| Original | Best Guess | Alternatives |
|----------|------------|--------------|
| [image] | "meeting" | "meeting", "meaning" |
| [image] | "Tuesday" | "Tuesday", "Thursday" |

⚠️ **Low confidence extraction - please verify manually**

Batch Processing

Batch OCR Job

## Batch OCR Processing

**Folder**: [Path]
**Total Documents**: [X]
**Status**: [In Progress/Complete]

### Results
| File | Pages | Confidence | Status |
|------|-------|------------|--------|
| doc1.pdf | 5 | 96% | ✅ Complete |
| doc2.pdf | 12 | 88% | ✅ Complete |
| doc3.pdf | 3 | 72% | ⚠️ Review |
| doc4.pdf | 8 | - | ❌ Failed |

### Issues
- doc3.pdf: Pages 2-3 have handwriting
- doc4.pdf: File corrupted

### Summary
- Successful: [X]
- Need Review: [Y]
- Failed: [Z]

Tool Recommendations

Cloud Services

• Google Cloud Vision (excellent accuracy)
• Amazon Textract (good for forms)
• Azure Computer Vision (balanced)
• Adobe Acrobat (integrated)

Desktop Software

• ABBYY FineReader (best accuracy)
• Adobe Acrobat Pro (reliable)
• Readiris (good value)
• Tesseract (free, open source)

Programming Libraries

• pytesseract (Python + Tesseract)
• EasyOCR (Python, multi-language)
• PaddleOCR (Python, good for Asian languages)

Limitations

• Cannot guarantee 100% accuracy
• Handwritten text has low accuracy
• Very small text may not extract well
• Decorative fonts are problematic
• Background images reduce quality
• Cannot read text in complex graphics
• Processing time increases with pages