PDF OCR Extraction
Skill433 starsChanged 10 months agoMITView on GitHub
Extract text from scanned PDFs using optical character recognition
In this repository137 other files it gives its agents.
- .cursor/rules/project-rules.mdcCursor rule
- academic-searchSkill
- ads-copywriterSkill
- ai-agent-builderSkill
- airtable-automationSkill
- ai-slidesSkill
- Amazon SellerSkill
- Apple Shortcuts IntegrationSkill
- Applicant ScreeningSkill
- Asana AutomationSkill
- batch-convertSkill
- batch-processorSkill
- Brand Guidelines GeneratorSkill
- Browser AutomationSkill
- calendar-automationSkill
- Changelog GeneratorSkill
- chart-designerSkill
- Chat with PDFSkill
- ClickUp AutomationSkill
- company-researchSkill
- competitive-analysisSkill
- Content Research WriterSkill
- contract-reviewSkill
- contract-templateSkill
- cover-letterSkill
- crm-automationSkill
- crypto-reportSkill
- customer-successSkill
- cv-builderSkill
- data-analysisSkill
- Database SyncSkill
- data-extractorSkill
- data-pipelineSkill
- dcf-valuationSkill
- deep-researchSkill
- devops-automationSkill
- dev-slidesSkill
- diagram-creatorSkill
- discord-botSkill
- doc-parserSkill
- doc-pipelineSkill
- DocuSign AutomationSkill
- docx-manipulationSkill
- Email ClassifierSkill
- email-drafterSkill
- email-marketingSkill
- ETL PipelineSkill
- excel-automationSkill
- expense-reportSkill
- Expense TrackerSkill
- Facebook/Meta AdsSkill
- File OrganizerSkill
- financial-modelingSkill
- form-builderSkill
- gmail-workflowsSkill
- google-ads-managerSkill
- Home Assistant AutomationSkill
- hr-automationSkill
- html-slidesSkill
- html-to-pptSkill
- The first 60 shown.
The file
Files
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 imagesPre-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 issuesHandwritten 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
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

