Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-End Unsupervised Document Image Blind Denoising   Removing noise from scanned pages is a vital step before their submission to
the 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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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: supr-con