Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Survey on Deep learning based Document Image Enhancement   Digitized documents such as scientific articles, tax forms, invoices,
contract papers, historic texts are widely used nowadays. These document images
could be degraded or damaged due to various reasons including poor lighting
conditions, shadow, distortions like noise and blur, aging, ink stain,
bleed-through, watermark, stamp, etc. Document image enhancement plays a
crucial role as a pre-processing step in many automated document analysis and
recognition tasks such as character recognition. With recent advances in deep
learning, many methods are proposed to enhance the quality of these document
images. In this paper, we review deep learning-based methods, datasets, and
metrics for six main document image enhancement tasks, including binarization,
debluring, denoising, defading, watermark removal, and shadow removal. We
summarize the recent works for each task and discuss their features,
challenges, and limitations. We introduce multiple document image enhancement
tasks that have received little to no attention, including over and under
exposure correction, super resolution, and bleed-through removal. We identify
several promising research directions and opportunities for future research.

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The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn