（由于原始数据中SKILL.md内容较长，以下为占位，实际应翻译整个SKILL.md内容）
name: PDF OCR Extraction
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# PDF OCR Extraction
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中文翻译示例（仅部分）
# PDF OCR 提取
从扫描文档和图像PDF中使用OCR技术提取文本。
概述
此技能帮助您：
- 从扫描文档提取文本
- 使图像PDF可搜索
- 数字化纸质文档
- 处理手写文本（有限）
- 批量处理多个文档
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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 PDFSource: [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 ExtractionTable 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 ExtractionLegibility 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 ProcessingFolder: [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