Skip to main content

A New Approach for Instance-Based Skew Estimation

  • Conference paper
  • 880 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6884))

Abstract

This paper proposes a new approach to a method to estimate a skew angle of a rotated document image. This is realized by using Speeded-Up Robust Features (SURF), and the goal is that it enables the image to be rotated back to the correct orientation. SURF detects a number of keypoints both from the reference image on which a set of standard alphabets (e.g. letter eaf through ezf in a certain font) are written, and the image of the rotated document. Two nearest features each from the reference image and the input image are compared to decide to how many degrees the feature in the input image is rotated. Finally the skew angle of the whole input image( the global skew angle) is decided by the majority of the total votes of angles that have been calculated as mentioned above.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. O’Gorman, L., Kasturi, R.: Document Image Analysis. IEEE Computer Society, Los Alamitos (1997)

    Google Scholar 

  2. Liang, J., Doermann, D., Li, H.: Camera-based analysis of text and documents: a survey. International Journal on Document Analysis and Recognition 7, 84–104 (2005)

    Article  Google Scholar 

  3. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Uchida, S., et al.: Skew Estimation by Instances. In: The Eighth IAPR International Workshop on Document Analysis Systems, pp. 201–208 (2008)

    Google Scholar 

  5. Pal, U., Mitra, M., Chaudhuri, B.B.: Multi-skew detection of Indian script documents. In: Proc. Int. Conf. Doc. Anal. Recog., pp. 292–296 (2001)

    Google Scholar 

  6. Ishitani, Y.: Document skew detection based on local region complexity. In: Proc. Int. Conf. Doc. Anal. Recog., pp. 49–52 (1993)

    Google Scholar 

  7. Jiang, X., Bunke, H., Widmer-Kljajo, D.: Skew detection of document images by focused nearest-neighbor clustering. In: Proc. Int. Conf. Doc. Anal. Recog., pp. 629–632 (1999)

    Google Scholar 

  8. Lu, Y., Tan, C.L.: Improved nearest neighbor based approach to accurate document skew estimation. In: Proc. Int. Conf. Doc. Anal. Recog., pp. 503–507 (2003)

    Google Scholar 

  9. Lu, S., Tan, C.L.: Camera document restoration for OCR. In: Workshop Camera-Based Doc. Anal. Recog., pp. 17–24 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Shiraishi, S., Feng, Y., Uchida, S. (2011). A New Approach for Instance-Based Skew Estimation. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23866-6_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23866-6_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23865-9

  • Online ISBN: 978-3-642-23866-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics