Improved neural network OCR based on preprocessed blob classes

Abstract

Optical character recognition (OCR) technologies have known an effervescent development in last decade. Development was strongly influenced by the development of hardware, advance in image processing, and classification algorithms. There are multiple OCR technologies available, each of them based on different approaches, e.g., geometric processing or cognitive learning based on neural networks. One critical parameter for each of those approaches is execution time. In our opinion a very important percent of text used to be “OCRed” is coming from preprinted documents and forms, which are bounded by various regulations in layout and/or contained information. Based on this observation we argue that most of the characters that must be recognized have a similar layout, thus improvement of the processing performance can be obtained by creating classes of similar characters (blobs) based on geometric similarities, and performing OCR only on the representative blob from each class. In this paper we will present the architecture of an OCR technology based on a multilayer neural network. Performance improvement has been obtained using a blob classifier that groups characters in classes, and then perform OCR only on the representative blob from each class.