What is Optical Character Recognition?
The computational conversion of scanned paper documents, photos, and raster images of text into machine-readable, searchable, and editable character strings.
Computer Vision Pre-Processing Pipeline
Modern OCR engines begin with adaptive image binarization (Otsu thresholding), skew angle correction, and noise filtering to isolate dark text glyphs from paper background textures.
Connected component analysis segments text lines and individual word bounding boxes, preparing glyph contours for neural feature extraction.
WebAssembly In-Browser Tesseract Engine
DocSwift compiles Tesseract C++ into WebAssembly, running complete multi-language OCR directly inside your browser memory without sending scanned documents to remote cloud servers.
Long Short-Term Memory (LSTM) recurrent neural networks evaluate character probability distributions alongside language dictionary models, achieving 99%+ accuracy on clean 300 DPI document scans.
Key Advantages & Impact
- Extracts editable text from physical book photos, receipts, and scanned PDF forms
- Enables full-text searchability on archived historical documents
- Executes 100% client-side via WebAssembly for complete confidentiality of identity cards
Common Misconceptions vs Reality
Experience This Technology in DocSwift Tools
Frequently Asked Questions
Does DocSwift OCR upload my scanned passport or ID card?
No. The entire OCR neural engine executes inside a WebAssembly Web Worker in your browser memory. Your ID scans never leave your device.