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PDF OCR Extraction

  • 1 installs
  • 6 repo stars
  • Updated January 30, 2026
  • claude-office-skills/skills-hub

extract text from scanned pdfs using optical character recognition

About

Pdf Ocr Extraction automates extract text from scanned pdfs using optical character recognition. Use it to integrate with your workflows and automation pipelines.

  • Extract text from scanned PDFs using optical chara...
  • Workflow integration and automation

PDF OCR Extraction by the numbers

  • 1 all-time installs (skills.sh)
  • Ranked #565 of 688 Office & Documents skills by installs in the Skillselion catalog
  • Data as of Jul 31, 2026 (Skillselion catalog sync)
npx skills add https://github.com/claude-office-skills/skills-hub --skill pdf-ocr-extraction

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Listed on Skillselion
Installs1
repo stars6
Last updatedJanuary 30, 2026
Repositoryclaude-office-skills/skills-hub

What it does

extract text from scanned pdfs using optical character recognition

Files

SKILL.mdMarkdownGitHub ↗

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 TypeExpected QualityTips
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

IssueSolution
Low resolutionUpscale image first
Skewed/rotatedAuto-deskew
Poor contrastAdjust levels/threshold
Noise/specksApply noise reduction
ShadowsFlatten lighting
Color documentConvert 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

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