[Feature] Add OCR-Based Scanned PDF to Searchable PDF Converter

[upasana-2006]

✨ Feature Overview

Add OCR-Based Scanned PDF to Searchable PDF Converter

Currently, the platform provides PDF conversion and image-processing utilities, but scanned PDFs that contain only images cannot be searched, copied, or indexed. Users often receive scanned documents such as notes, invoices, contracts, research papers, and forms where the text is embedded inside images rather than stored as actual text.

This feature will introduce a Scanned PDF to Searchable PDF Converter that uses Optical Character Recognition (OCR) to extract text from scanned pages and generate a new PDF containing an invisible text layer. The visual appearance of the original document will remain unchanged while allowing users to search, highlight, copy, and index text within the PDF.

The feature will work completely offline using local OCR libraries and will support multi-page scanned documents.

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🚀 Why is this Feature Needed?

Many real-world PDFs are generated through scanners and consist entirely of images. These files present several limitations:

• Users cannot search for keywords within the document.
• Copying and extracting text is impossible.
• Screen readers and accessibility tools cannot interpret content.
• Search engines and document management systems cannot index the document properly.
• Large archives of scanned documents become difficult to organize and retrieve.

Adding OCR-based searchable PDF generation would provide significant benefits:

• Improved accessibility and usability.
• Better document search capabilities.
• Easier content extraction for educational and professional use.
• Enhanced support for scanned notes, books, invoices, reports, and forms.
• A more complete PDF-processing toolkit within the project.

This feature aligns well with the repository's existing focus on PDF and image-processing utilities while expanding its real-world usefulness.

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🎨 Visuals (If applicable)

Current Workflow

Scanned PDF
      ↓
Image-only Pages
      ↓
Cannot Search Text
      ↓
Limited Usability

Proposed Workflow

Scanned PDF
      ↓
Page Rendering
      ↓
Image Preprocessing
      ↓
OCR Extraction
      ↓
Searchable PDF Generation
      ↓
Search / Copy / Highlight Text

Example

Before:

• Searching "Invoice Number" returns no results.
• Text cannot be selected.

After:

• Searching "Invoice Number" finds the text instantly.
• Users can copy and highlight text normally.

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🔧 Possible Implementation (Optional)

Backend Processing Pipeline

1. Upload scanned PDF.

2. Render PDF pages using PyMuPDF.

3. Preprocess images using OpenCV:

   • Grayscale conversion
   • Noise removal
   • Adaptive thresholding
   • Optional deskewing

4. Run OCR using Tesseract (pytesseract).

5. Extract text and positional information.

6. Generate a searchable PDF by overlaying an invisible text layer while preserving the original page images.

7. Return the generated PDF to the user.

Suggested Libraries

PyMuPDF (fitz)
OpenCV
pytesseract
Pillow
reportlab

Suggested File Structure

backend/
├── blueprints/
│   └── pdf_ocr_searchable.py

├── utils/
│   └── ocr_preprocess.py

├── services/
│   └── searchable_pdf_service.py

└── tests/
    └── test_pdf_ocr_searchable.py

Optional Enhancements

• Multi-language OCR support.
• OCR confidence reporting.
• Page-level OCR statistics.
• Automatic language detection.
• Batch PDF processing.
• OCR quality enhancement presets.

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💡 Additional Notes

• The feature should process files entirely in memory and avoid permanently storing user documents.

• OCR operations should support multi-page PDFs efficiently.

• Error handling should be included for:

  • Empty PDFs
  • Corrupted PDFs
  • Unsupported file types
  • OCR failures

• Unit tests should be added for successful conversions and edge cases.

• The implementation should remain independent of external OCR APIs and use local open-source libraries only.

Acceptance Criteria

• Accept scanned/image-based PDFs.
• Generate searchable PDFs with selectable text.
• Preserve original document appearance.
• Support multi-page PDFs.
• Work without external OCR services.
• Include automated tests.
• Handle invalid inputs gracefully.
• Maintain user privacy by avoiding persistent file storage.

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🏆 Are you contributing under any open-source program?

Yes — GSSoC 2026 (GirlScript Summer of Code 2026).

[upasana-2006]

/assign
@Durgeshwar-AI please look into this issue and if it looks feasible assign it to me