Document Image Restoration Using Bayesian Inference Method
Yan Pei · Jisuanji fangzhen · 2011
Restoration of documents is a key step for applications in document processing,retrieval understanding as well as digital libraries,for example as in book readers.In this paper,we present a method to restore document images,by using a Maximum a Posteriori(MAP) framework.The prior probability of the characters is learned from the training document images.The extraction of a single high-quality enhanced text image from a set of degraded images can benefit from a strong prior knowledge.The restoration process should allow for discontinuities and discourage oscillations at the same time.These properties were represented in a total variation based prior model.Results indicate that our method is appropriate for document image restoration,where resolution enhancement is an added gain.