Scanned images present unique challenges to getting good data from documents. In order to target that data, images must be run through Optical Character Recognition (OCR) to convert pixels on the page into machine readable text. Poor image quality and limitations of standard OCR processes can produce less than desirable results.

This course aims to educate users on different methods available in Grooper to improve image quality through image processing and leverage Grooper’s unique approach to OCR to get the best text data from documents. Students will gain a practical understanding of how to build image processing and OCR profiles together to improve the accuracy of standard OCR. Furthermore, students will learn how Grooper’s image processing provides additional visual based information (such as table lines, barcodes, and checkboxes) and how to make use of it.