Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CVPR.2008.4587820 | CVPR |
Keywords | Field | DocType |
training data collection,dual-layer markov random field,old documents,ink-bleed reduction,image resolution,foreground strokes,image pixels,image alignment,markov processes,mrf,document image processing,aging,data collection,interference,humidity,pixel,application software,foreground background,principal component analysis,chemicals,ink | Computer vision,Markov process,Pattern recognition,Markov random field,Computer science,Interference (wave propagation),Artificial intelligence,Pixel,Bleed,Application software,Image resolution,Computation | Conference |
Volume | Issue | ISSN |
2008 | 1 | 1063-6919 E-ISBN : 978-1-4244-2243-2 |
ISBN | Citations | PageRank |
978-1-4244-2243-2 | 18 | 0.92 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Huang | 1 | 18 | 0.92 |
| Michael S. Brown | 2 | 2122 | 129.13 |
| Dong Xu | 3 | 7616 | 291.96 |