A Framework for Reducing Ink-Bleed in Old Documents

CVPR 2008

doi:10.1109/CVPR.2008.4587820 /cvpr/2008/huang2008cvpr-framework/

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.

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Cite

Text

Huang et al. "A Framework for Reducing Ink-Bleed in Old Documents." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587820

Markdown

[Huang et al. "A Framework for Reducing Ink-Bleed in Old Documents." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/huang2008cvpr-framework/) doi:10.1109/CVPR.2008.4587820

BibTeX

@inproceedings{huang2008cvpr-framework,
  title     = {{A Framework for Reducing Ink-Bleed in Old Documents}},
  author    = {Huang, Yi and Brown, Michael S. and Xu, Dong},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2008},
  doi       = {10.1109/CVPR.2008.4587820},
  url       = {https://mlanthology.org/cvpr/2008/huang2008cvpr-framework/}
}