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I have some documents containing some text (machine writing text) that I intend to apply OCR on them in order to extract the text.

The problem is that these documents contain a lot of noise but in different ways (some documents have noise in the middle, others in the top, etc.), which means that I can't apply simple thresholding in order to remove the noise (i.e applying simple threshold does not only remove the noise, but it removes some parts of the text).

For these reasons, I thought about using AI to de-noise the documents.

Does anyone know if it is possible to do that with AI or any alternative way?

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This is also the topic of Image Processing (which has analytical solutions instead of learning) mostly through predesigned filters. The filter depends on the type of noise, (salt & pepper, Gaussian, etc.) i.e., for salt & pepper choosing the median in a window. There are a lot of denoising research in literature. There are also more recent learning based denoising applications, but it requires data so that you can train.

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    $\begingroup$ thank you for you clarification, Do you recall any articles or links to start with? It is OK for the data, I can provide data for training and testing. $\endgroup$
    – singrium
    Jan 24, 2019 at 8:38
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    $\begingroup$ This seems as a good article: papers.nips.cc/paper/… . Also, inpainting might be needed if occlusions are too much. If you want analytic solutions, you can also look up sparse representations/dictionary learning methods for denoising/inpainting. $\endgroup$
    – user
    Jan 24, 2019 at 9:58
  • $\begingroup$ Thank you, I'll start from what you mentioned! $\endgroup$
    – singrium
    Jan 24, 2019 at 10:11
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    $\begingroup$ Also, I forgot to mention, but if you specifically target documents, then you can narrow down your research. Some lookup returned these that look promising: Kaggle competition on what you ask. kaggle.com/c/denoising-dirty-documents , A paper on removing corruption in scanned documents. unitec.researchbank.ac.nz/handle/10652/2730 $\endgroup$
    – user
    Jan 24, 2019 at 11:25
  • $\begingroup$ The Kaggle competion sounds promising, thank you for the sources! $\endgroup$
    – singrium
    Jan 24, 2019 at 11:27

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