Browse State-of-the-Art › Document Shadow Removal
Document Shadow Removal
15 papers with code · 0 benchmarks · 3 datasets archive 2025-07-28
Document shadow removal refers to the process of eliminating or reducing the appearance of shadows in scanned or photographed documents. Shadows can occur due to various factors, such as uneven lighting, folds in the paper, or the presence of objects casting shadows during the scanning or capturing process.
Removing shadows from documents is important because they can degrade the readability and quality of the content. Shadows can obscure text or graphics, making it difficult to extract accurate information from the document. By eliminating shadows, the document becomes more legible and suitable for various purposes, including optical character recognition (OCR), document analysis, and archival purposes.
Document shadow removal techniques typically involve image processing and enhancement algorithms. These algorithms analyze the image and identify regions that contain shadows. They then adjust the brightness, contrast, and other image properties in the shadowed areas to minimize or eliminate the shadow effect. This process often requires advanced image analysis and manipulation techniques, such as histogram equalization, adaptive filtering, and image segmentation.
In recent years, machine learning and deep learning approaches have also been applied to document shadow removal. These methods utilize large datasets of shadowed and non-shadowed documents to train models that can automatically detect and remove shadows from new images. The models learn to recognize the characteristics of shadows and generate shadow-free versions of the documents.
Overall, document shadow removal plays a crucial role in improving the quality and legibility of scanned or photographed documents, making them more suitable for various applications in areas such as digital archiving, document analysis, and information extraction.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
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Datasets archive 2025-07-28
3 datasets whose archive record lists this task, ordered by the archive's paper count.
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Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
15 shown of 15 papers with code (17 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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27 Aug 2023 2 repositories listedWe handle high-resolution document shadow removal directly via a larger-scale real-world dataset and a carefully designed frequency-aware network.
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1 Jun 2020 2 repositories listedFor taking advantage of specific properties of document images, a background estimation module is designed for extracting the global background color of the document.
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22 Apr 2019 2 repositories listedIn this paper, we propose a novel algorithm to rectify illumination of the digitized documents by eliminating shading artifacts.
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11 Mar 2017 2 repositories listedIn this work, we automatically detect and remove distracting shadows from photographs of documents and other text-based items.
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13 Sep 2023 1 repository listedThe STD module employs a traditional thresholding technique and leverages the attention mechanism of the Transformer to gather global information, thereby enabling precise detection of shadow masks.
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5 May 2023 1 repository listedThirdly, we propose an adaptive text contrast enhancement strategy to generate shadow-free results with comfortable visual perception across shadow and non-shadow regions.
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22 Mar 2023 1 repository listedDocument shadow removal is an integral task in document enhancement pipelines, as it improves visibility, readability and thus the overall quality.
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1 Jan 2023 1 repository listedIn this paper, we present a color-aware background extraction network (CBENet) for extracting a spatially varying background image that accurately depicts the background colors of the document.
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30 Nov 2022 1 repository listedShadow removal improves the visual quality and legibility of digital copies of documents.
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18 Oct 2022 1 repository listedIn this paper, we present a large-scale and diverse dataset called fully synthetic document shadow removal dataset (FSDSRD) that does not require capturing documents.
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4 Dec 2020 1 repository listedThe proposed method can remove the shading artifacts and outperform some state-of-the-art methods, especially for the removal of shadow boundaries.
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29 Nov 2020 1 repository listedHowever, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and…
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26 Aug 2019 1 repository listedThis paper proposes an effective method to remove shadows from the single document images, which contains two stages: shadow detection and shadow removal.
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13 Sep 2018 1 repository listedUneven illumination and shadows in document images cause a challenge for digitization applications and automated workflows.
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1 Jun 2018 1 repository listedInterestingly, this information is based on a solution to a seemingly unrelated problem of visibility detection in R3.
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