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]
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- Pages Processed
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- [X]
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- Language
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- [Detected/Specified]
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- Confidence
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- [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 | |
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| | 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 | |
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| | [Data] | [Data] | [Data] |
Uncertain Text | Page | Original | Confidence | Possible | |
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| | 3 | "teh" | 70% | "the" | | 5 | "l0ve" | 65% | "love" | Searchable PDF Output
- OCR to Searchable PDF
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- Source
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- [filename.pdf]
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- Output
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- [filename_searchable.pdf]
Processing Summary | Metric | Value | |
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| | 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
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- 300 DPI minimum (600 for small text)
- Contrast
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- Clear black text on white background
- Alignment
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- Document is straight (not skewed)
- Completeness
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- No cut-off edges
- Cleanliness
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- 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
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- English, Spanish, French, German, Italian
- Good
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- Chinese (Simplified/Traditional), Japanese, Korean
- Moderate
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- 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 | |
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| | Name | John Smith | 98% | | Date | 01/15/2026 | 95% | | Address | 123 Main St | 92% |
Checkboxes | Question | Checked | |
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| | 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 | |
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- Value 1
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- Value 2
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- Value 3
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- Value 4
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- Value 5
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- Value 6
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- Table confidence
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- 85%
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- Note
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- Column 3 may have alignment issues Handwritten Text
- Handwritten Text Extraction
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- Legibility Assessment
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- [Good/Fair/Poor]
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- Recommended
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- Manual review
Extracted Text (Confidence: 65%) [Extracted text with uncertain words marked]
Uncertain Words | Original | Best Guess | Alternatives | |
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| | [image] | "meeting" | "meeting", "meaning" | | [image] | "Tuesday" | "Tuesday", "Thursday" | ⚠️ ** Low confidence extraction - please verify manually ** Batch Processing Batch OCR Job
- Batch OCR Processing
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- Folder
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- [Path]
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- Total Documents
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- [X]
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- Status
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- [In Progress/Complete]
Results | File | Pages | Confidence | Status | |
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| | 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