Optical Character Recognition (OCR) Explained: Extracting Text from Scanned PDFs Privately

Scanned documents, paper receipts, and smartphone photos of invoices share one common flaw: They are static image files wrapped inside a PDF envelope. Because there is no underlying text stream, you cannot search for words, highlight key sections, or copy text into other applications.

This is where Optical Character Recognition (OCR) comes to the rescue. OCR bridges the gap between physical paper and editable digital text.

How OCR Works: The 4-Step Process

1. Image Pre-processing: The OCR engine enhances contrast, cleans up visual noise, binarizes pixels into pure black and white, and corrects skew (deskewing).
2. Layout & Feature Analysis: The computer vision model breaks down the image into distinct blocks: paragraphs, tables, images, and individual line bounds.
3. Character Recognition: Feature extraction matches pixel patterns against character shapes, fonts, and neural network stroke weights.
4. Post-processing & Spell Correction: Natural Language Processing (NLP) models verify word contexts to correct common OCR misreadings (such as confusing '0' with 'O' or '1' with 'l').

Running OCR Locally in the Browser

In the past, running OCR required heavy desktop software installations or sending sensitive scanned documents to cloud server APIs. Today, WebAssembly (WASM) enables full Tesseract OCR engines to compile directly into web browser workers.

Using our Extract Text (OCR) Tool, your browser downloads the recognition model once, allowing you to convert scanned documents to plain text or Markdown with zero privacy risks.