Back to Guides
AI & ML Sep 12, 2026 8 min read

How Optical Character Recognition (OCR) Works

Explore the fascinating technology behind OCR, how machine learning extracts text from images, and practical applications in modern software.

The Magic of Optical Character Recognition

Optical Character Recognition (OCR) is the electronic conversion of images of typed, handwritten, or printed text into machine-encoded text.

How it Works

Modern OCR systems rely heavily on Machine Learning and Neural Networks. The pipeline generally involves:

  1. Pre-processing: The image is converted to grayscale, binarized (pure black and white), and deskewed (straightened).
  2. Character Isolation: The software analyzes the image to identify lines of text, then words, then individual character boundaries.
  3. Pattern Recognition: The system compares the isolated character images against thousands of known character shapes in its trained model to determine the most likely match.
  4. Post-processing: The system uses linguistic context (dictionaries) to correct probable errors (e.g., recognizing that "1" should be "l" in the word "apple").

With the power of WebAssembly, you can now run complex ML models entirely in your browser. Try extracting text from an image locally using our OCR Studio.