OCR (Optical Character Recognition) seems like magic—point at text, get editable characters. Here's how it actually works.
The OCR Process
Step 1: Image Preprocessing
- Noise reduction: Remove artifacts and grain
- Binarization: Convert to black and white
- Deskewing: Straighten tilted text
- Scaling: Normalize image size
Step 2: Layout Analysis
- Identify text regions vs. images
- Detect text lines and word boundaries
- Determine reading order
- Separate columns if present
Step 3: Character Recognition
Modern OCR uses two approaches:
Pattern Matching
- Compare character shapes to stored templates
- Fast but limited to known fonts
Feature Extraction + AI
- Analyze character features (curves, lines, intersections)
- Use machine learning to classify characters
- Handle varied fonts and handwriting
Step 4: Post-Processing
- Spell checking and correction
- Context analysis
- Confidence scoring
- Output formatting
What OCR Sees
Image pixels → Edge detection → Shape analysis → Character matching → Text output
Accuracy Factors
| Factor | Impact |
|---|---|
| Image resolution | Higher = better |
| Text clarity | Sharper = better |
| Font type | Standard fonts best |
| Contrast | Higher = better |
| Noise | Less = better |
Modern AI OCR
Today's OCR uses deep learning:
- Neural networks trained on millions of text images
- Can handle multiple languages
- Recognizes degraded and stylized text
- Improves with more training data
What OCR Can't Do Well
- Very decorative fonts
- Extremely small text
- Heavily degraded documents
- Complex handwriting
- Text on busy backgrounds
Privacy in Rune's OCR
All this processing happens in your browser using JavaScript and WASM. No server involvement means complete privacy.
Conclusion
OCR combines image processing and AI to convert visual text to data. Rune's Image to Text (OCR) brings this technology to your browser with no uploads required.