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• W2156821767 abstract "We describe a novel application framework to reduce the effects of ink-bleed in old documents. This task is treated as a classification problem where training-data is used to compute per-pixel likelihoods for use in a dual-layer Markov Random Field (MRF) that simultaneously labels image pixels of the front and back of a document as either foreground, background, or ink-bleed, while maintaining the integrity of foreground strokes. Our approach obtains better results than previous work without the need for assumptions about ink-bleed intensities or extensive parameter tuning. Our overall framework is detailed, including front and back image alignment, training-data collection, and the MRF formulation with associated likelihoods and intra- and interlayer cost computations." @default.
• W2156821767 created "2016-06-24" @default.
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• W2156821767 date "2008-06-01" @default.
• W2156821767 modified "2023-09-27" @default.
• W2156821767 title "A framework for reducing ink-bleed in old documents" @default.
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• W2156821767 doi "https://doi.org/10.1109/cvpr.2008.4587820" @default.
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