The State of the Art of Document Image Degradation Modelling

The literature on models of document image degradation is reviewed, and open problems are listed. In response to the unpleasant fact that the accuracy of document recognition algorithms falls drastically when image quality degrades even slightly, researchers in the last decade have intensiied their study of explicit, quantitative, parameter-ized models of image defects that occur during printing and scanning. Several models have been proposed, some motivated by the physics of image formation and others by the surface statistics of image distributions. A wide range of techniques for estimating parameters of these models has been explored. These models, in the form of pseudo-random generators of synthetic images, permit, for the rst time, investigations into fundamental properties of concrete image recognition problems including the Bayes error of problems and the asymptotic accuracy and domain of competency of classiier technologies. The use of massive sets of synthetic images, in the construction and testing of high-performance classiiers, has accelerated in the last few years. Open problems include the search for methods for comparing competing models and sound methodologies for the use of synthetic data in engineering.