Data scientists classify different types of OCR technologies based on their use and application. The following are a few examples:
Simple optical character recognition software
A simple OCR engine works by storing many different font and text image patterns as templates. The OCR software uses pattern-matching algorithms to compare text images, character by character, to its internal database. If the system matches the text word by word, it is called optical word recognition. This solution has limitations because there are virtually unlimited font and handwriting styles, and every single type cannot be captured and stored in the database.
Intelligent character recognition software
Modern OCR systems use intelligent character recognition (ICR) technology to read the text in the same way humans do. They use advanced methods that train machines to behave like humans by using machine learning software. A machine learning system called a neural network analyzes the text over many levels, processing the image repeatedly. It looks for different image attributes, such as curves, lines, intersections, and loops, and combines the results of all these different levels of analysis to get the final result. Even though ICR typically processes the images one character at a time, the process is fast, with results obtained in seconds.
Intelligent word recognition
Intelligent word recognition systems work on the same principles as ICR, but process whole word images instead of preprocessing the images into characters.
Optical mark recognition
Optical mark recognition identifies logos, watermarks, and other text symbols in a document.