OCR provides us with different ways to see an image
Almost all companies -- especially certain ones in some service areas -- struggle with processing inundating volume of incoming paper documents. In most cases this requires time consuming and costly human work of various quality.
The most typical task is extracting relevant pieces of information from digitalized papers and channel them to traditional tabular data tables or spreadsheets for storing or further processing; this is where optical character recognition comes into the picture with the help of image processing and deep learning. The actual implementation and the difficulty level depend on the nature of the specific task: whether the document contains handwritten or printed texts, we face with some kind of known predefined layouts or any kind of formats. The approach used to have different stage: first localization of the relevant texts (like filled out formulars) on the document at large, then the actual character recognition to turn pixels into strings, then optionally some post processing -- like dictionary or rule-based corrections -- for improving the final results.
Added values (Why AI/ML/DL):automatization of paper document processing for saving cost and time.