What is OCR?
Optical Character Recognition (OCR) converts images of text into machine-readable text. For PDFs, this means transforming scanned documents from "pictures of text" into actual searchable, selectable text.
How Modern OCR Works
Today's OCR engines use machine learning:
- β’Image preprocessing: Deskewing, noise removal, binarization
- β’Text detection: Finding regions containing text
- β’Character recognition: Neural networks identify each character
- β’Language modeling: Context improves accuracy (e.g., "tbe" β "the")
- β’Layout analysis: Preserving columns, tables, reading order
OCR Accuracy Factors
Recognition accuracy varies significantly:
| Document Type | Typical Accuracy | Notes |
|---|---|---|
| Clean printed text (300 DPI) | 99%+ | Nearly perfect |
| Newspaper/book scans | 95-98% | Some errors in small text |
| Faded or low-contrast | 80-95% | Preprocessing helps |
| Handwriting | 60-85% | Highly variable |
| Mixed languages | 90-97% | Depends on language pair |
Language Support
OCR engines support different languages with varying accuracy. Latin-alphabet languages work best. Chinese, Japanese, and Korean require specialized models. Our tool supports English, Chinese (Simplified), and mixed documents.
When to Use OCR
Use OCR when you have:
- β’Scanned paper documents
- β’Photos of documents
- β’PDFs where you can't select text
- β’Image-based PDFs from older scanners
OCR Output Options
After OCR, you can get: searchable PDF (invisible text layer over images), plain text file, or Word document. The searchable PDF preserves visual appearance while adding text functionality.
