Removing Shadows from Images of Documents

  • Steve BakoEmail author
  • Soheil Darabi
  • Eli Shechtman
  • Jue Wang
  • Kalyan Sunkavalli
  • Pradeep Sen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)


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.


Document Image Background Color Shadow Region Binarization Method Ground Truth Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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 License), and Momofuku - Menu w/ Shadow puppets by Lawrence. Please see supplementary materials for links to the images and license.

Supplementary material

416261_1_En_12_MOESM1_ESM.pdf (10.1 mb)
Supplementary material 1 (pdf 10315 KB)


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Steve Bako
    • 1
    Email author
  • Soheil Darabi
    • 2
  • Eli Shechtman
    • 2
  • Jue Wang
    • 2
  • Kalyan Sunkavalli
    • 2
  • Pradeep Sen
    • 1
  1. 1.University of California, Santa BarbaraSanta BarbaraUSA
  2. 2.Adobe ResearchSeattleUSA

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