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SmartDCap: semi-automatic capture of higher quality document images from a smartphone

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Published:19 March 2013Publication History

ABSTRACT

People frequently capture photos with their smartphones, and some are starting to capture images of documents. However, the quality of captured document images is often lower than expected, even when an application that performs post-processing to improve the image is used. To improve the quality of captured images before post-processing, we developed the Smart Document Capture (SmartDCap) application that provides real-time feedback to users about the likely quality of a captured image. The quality measures capture the sharpness and framing of a page or regions on a page, such as a set of one or more columns, a part of a column, a figure, or a table. Using our approach, while users adjust the camera position, the application automatically determines when to take a picture of a document to produce a good quality result. We performed a subjective evaluation comparing SmartDCap and the Android Ice Cream Sandwich (ICS) camera application; we also used raters to evaluate the quality of the captured images. Our results indicate that users find SmartDCap to be as easy to use as the standard ICS camera application. Also, images captured using SmartDCap are sharper and better framed on average than images using the ICS camera application.

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References

  1. Brewster, S., Wright, P., and Edwards, A. An evaluation of earcons for use in auditory human-computer interfaces. In Proc. of the INTERACT '93 and CHI '93 Conf. on Human Factors in Computing Systems, ACM (1993), 222--227. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Carter, S., Adcock, J., Doherty, J., and Branham, S. Nudgecam: toward targeted, higher quality media capture. In Proc. of the ACM Intl. Conf. on Multimedia (2010), 615--618. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Ferzli, R., and Karam, L. A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Transactions on Image Processing 18, 4 (2009), 717--728. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Garzonis, S., Jones, S., Jay, T., and ONeill, E. Auditory icon and earcon mobile service notifications: intuitiveness, learnability, memorability and preference. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, ACM (2009), 1513--1522. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Ha, J., Haralick, R., and Phillips, I. Recursive x-y cut using bounding boxes of connected components. In Proc. of the Intl. Conf. on Document Analysis and Recognition, vol. 2, IEEE (1995), 952--955. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Hoggan, E., Crossan, A., Brewster, S., and Kaaresoja, T. Audio or tactile feedback: which modality when? In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, ACM (2009), 2253--2256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Jayant, C., Ji, H., White, S., and Bigham, J. P. Supporting blind photography. In Proc. of the ACM SIGACCESS Conf. on Computers and Accessibility (2011), 203--210. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Larson, E., and Chandler, D. Most apparent distortion: Full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging 19, 1 (2012).Google ScholarGoogle Scholar
  9. Lee, S., and Ryu, D. Parameter-free geometric document layout analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 11 (2001), 1240--1256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Liang, J., Doermann, D., and Li, H. Camera-based analysis of text and documents: a survey. Intl. Journal on Document Analysis and Recognition 7, 2 (2005), 84--104.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Liu, C., Huot, S., Diehl, J., Mackay, W., and Beaudouin-Lafon, M. Evaluating the benefits of real-time feedback in mobile augmented reality with hand-held devices. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, ACM (2012), 2973--2976. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Narvekar, N., and Karam, L. A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Transactions on Image Processing 20, 9 (2011), 2678--2683. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Nees, M., and Walker, B. Data density and trend reversals in auditory graphs: Effects on point-estimation and trend-identification tasks. ACM Transactions on Applied Perception (TAP) 5, 3 (2008), 13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Pollard, S., and Pilu, M. Building cameras for capturing documents. Intl. Journal on Document Analysis and Recognition 7, 2 (2005), 123--137.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Shafait, F., Keysers, D., and Breuel, T. Performance evaluation and benchmarking of six-page segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 6 (2008), 941--954. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Sheikh, H., Sabir, M., and Bovik, A. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing 15, 11 (Nov 2006), 3440--3451. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Vázquez, M., and Steinfeld, A. Facilitating photographic documentation of accessibility in street scenes. In CHI Extended Abstracts on Human Factors in Computing Systems, ACM (2011), 1711--1716. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Walker, B., and Kramer, G. Mappings and metaphors in auditory displays: An experimental assessment. ACM Transactions on Applied Perception (TAP) 2, 4 (2005), 407--412. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. White, S., Ji, H., and Bigham, J. Easysnap: Real-time audio feedback for blind photography. In Adj. Proc. of the ACM Symposium on User Interface Software and Technology (2010), 409--410. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Yu, Y., and Liu, Z. A user study of visual versus sonically-enhanced interfaces for use while walking. In Proc. of the ACM Intl. Conf. on Multimedia (2010), 687--680. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        cover image ACM Conferences
        IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces
        March 2013
        470 pages
        ISBN:9781450319652
        DOI:10.1145/2449396

        Copyright © 2013 ACM

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        Publication History

        • Published: 19 March 2013

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        IUI '13 Paper Acceptance Rate43of192submissions,22%Overall Acceptance Rate746of2,811submissions,27%

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