Abstract
In this work, we automatically detect and remove distracting shadows from photographs of documents and other text-based items. Documents typically have a constant colored background; based on this observation, we propose a technique to estimate background and text color in local image blocks. We match these local background color estimates to a global reference to generate a shadow map. Correcting the image with this shadow map produces the final unshadowed output. We demonstrate that our algorithm is robust and produces high-quality results, qualitatively and quantitatively, in both controlled and real-world settings containing large regions of significant shadow.
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Notes
- 1.
We exclude Acrobat “Enhance” from the table as it requires manual interaction for each image.
- 2.
The DIBCO 2013 dataset provides ground truth images to evaluate the performance of binarization algorithms. Furthermore, almost all of the images in this dataset are already shadow-free, so we report PSNR on just a single example containing shadow.
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Acknowledgement
This work was supported in part by NSF awards IIS-1321168 and RI-1619376 and by a gift from Adobe. We thank Daniel Oliveira for providing code to run comparisons. The images in Figs. 3, 5 and 6 were used through the Creative Commons 2.0 License without modification. The title and photographers from Flickr (unless otherwise noted) in order of appearance are: Open Textbook Summit 2014 by BCcampus_News, Army in the Shadows, Army in the Light by Cuzco84, That Please by Kimli, Medieval text in the Christ Church Archive by -JvL-, Untitled by Jacek.NL, Cartmel Priory by Rosscophoto, Transfer Damaged Textbook by Enokson, Untitled by Colin Manuel, find by PHIL, Declaration of Independence photo by taliesin at Morguefile.com (Morguefile License), and Momofuku - Menu w/ Shadow puppets by Lawrence. Please see supplementary materials for links to the images and license.
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Bako, S., Darabi, S., Shechtman, E., Wang, J., Sunkavalli, K., Sen, P. (2017). Removing Shadows from Images of Documents. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10113. Springer, Cham. https://doi.org/10.1007/978-3-319-54187-7_12
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