Ocr classification based on transition ground data

Abstract

An OCR system 10 classifies an input image vector of an unclassified symbol with respect to a library 14T of template image vectors of pre-classified characters. Each template vector is in the form of a sequence of elements representing the image intensity level of a pixel within the character defined by that template vector. Each template element is part of the image background, foreground, or transition ground between the background and foreground. Each input vector, like the template vectors, is also in the form of a sequence of elements. However, in the input vector, each element represents the sum or an image intensity level signal component defining the symbol within the image of the unclassified symbol plus a greyscale noise component. Each input element is also part of the background, foreground, or transition ground. The input vector and at least one of the template vectors are entered into a classifier device 18. The input vector is classified based on the backgrounds, foregrounds, and transition grounds. The presence of transition ground in the input vector and the template vector produces a robust classification response with a more uniform correlation coefficient between repeated classifications of the same input symbol. The classifier device may be a distance function classifier or a neural network classifier.