Markov random field (MRF) based approaches have been shown to perform well in a wide range of applications. Due to the iterative nature of the algorithm, the computational cost of such applications is normally high. In the context of document image analysis, where numerous documents have to be processed, this computational cost may become prohibitive. We describe a novel approach to document image enhancement using MRF.We show that by using domain specific knowledge, we are able to substantially improve computational performance by an order of magnitude. Moreover, in contrast to known techniques where patch initialization is arbitrary, in the proposed approach patch initialization is data consistent and so results in improved effectiveness. Experimental results comparing the proposed approach to known techniques using historical documents from the Frieder Collection are provided.