Classifier for evaluating the effects of image processing on character recognition
James F. McNamara, David W. Casey, ROBERT WILLIAM SMITH, David S. Bradburn · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
This paper presents an automated methodology for selecting morphological filters from a given set that will most improve a text image for character recognition. Toward this end, a classifier is described which generates an internal representation of the image qualities affecting readability, and which uses those properties to identify images that will benefit by application of a particular filter. In the study, handprint and machineprint character bitmaps are taken from binarized document images and enhanced using a set of non-recursive neighborhood operators. Features related to the connected components and their morphology are extracted prior to the filtering step. Character recognition results are obtained from commercially available recognition engines, which together with the measured morphological features, form a training set for statistical classifiers. The classifiers derive a partitioning of the input based on the morphological features, and the output yields an indication of the specific filter most appropriate to apply to improve character recognition. Results are presented for handprinted ZIP code digit images, and for average to poor quality and dot matrix alphanumeric machineprint obtained from postal application images. Performance for each case is statistically analyzed.