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This paper presents a novel user-assisted approach to reduce ink-bleed interference found in old manuscripts. The problem is addressed by first having the user provide simple examples of foreground ink, ink-bleed, and the manuscript's background. From this small amount of user-labeled data, likelihoods of each pixel being foreground, ink-bleed, or background are computed and used as the data costs of a dual-layer Markov random field (MRF) that simultaneously labels all pixels in both the front and back sides of the manuscript. This user-assisted approach produces better results than existing algorithms without the need for extensive parameter tuning or prior assumptions about the ink-bleed intensity characteristics. Our overall application framework is discussed along with details of the features used in the data costs, a comparison between K-nearest neighbor and support vector machine for likelihood estimation, the dual-layer MRF formulation with associated inter- and intra-layer costs, and a comparison of our approach against other ink-bleed reduction algorithms.