End-to-End Unsupervised Document Image Blind Denoising

Mehrdad J. Gangeh, Marcin Plata, Hamid R. Motahari Nezhad, Nigel P Duffy

ICCV 2021 pp. 7888-7897

doi:10.1109/ICCV48922.2021.00779 /iccv/2021/gangeh2021iccv-endtoend/

Abstract

Removing noise from scanned pages is a vital step before their submission to optical character recognition (OCR) system. Most available image denoising methods are supervised where the pairs of noisy/clean pages are required. However, this assumption is rarely met in real settings. Besides, there is no single model that can remove various noise types from documents. Here, we propose a unified end-to-end unsupervised deep learning model, for the first time, that can effectively remove multiple types of noise, including salt & pepper noise, blurred and/or faded text, as well as watermarks from documents at various levels of intensity. We demonstrate that the proposed model significantly improves the quality of scanned images and the OCR of the pages on several test datasets.

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Cite

Text

Gangeh et al. "End-to-End Unsupervised Document Image Blind Denoising." International Conference on Computer Vision, 2021. doi:10.1109/ICCV48922.2021.00779

Markdown

[Gangeh et al. "End-to-End Unsupervised Document Image Blind Denoising." International Conference on Computer Vision, 2021.](https://mlanthology.org/iccv/2021/gangeh2021iccv-endtoend/) doi:10.1109/ICCV48922.2021.00779

BibTeX

@inproceedings{gangeh2021iccv-endtoend,
  title     = {{End-to-End Unsupervised Document Image Blind Denoising}},
  author    = {Gangeh, Mehrdad J. and Plata, Marcin and Nezhad, Hamid R. Motahari and Duffy, Nigel P},
  booktitle = {International Conference on Computer Vision},
  year      = {2021},
  pages     = {7888-7897},
  doi       = {10.1109/ICCV48922.2021.00779},
  url       = {https://mlanthology.org/iccv/2021/gangeh2021iccv-endtoend/}
}