Skew Estimation for Unconstrained Handwritten Documents

  • V. N. Manjunath Aradhya
  • C. Naveena
  • S. K. Niranjan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 192)


Document skew estimation is one of the most important and challenging phase in OCR system. Skew estimation of handwritten documents is still remains challenging in the field of document image analysis due to a non-uniform text line. Hence, in this paper, we present a novel scheme for handwritten documents. The proposed method is based on mixture models. The expectation-maximization (EM) algorithm is used to learn the mixture of Gaussians. Subsequently the cluster means obtained from the individual words is used estimate the skew angle. Experiments on different handwritten documents and documents corrupted by noise shows the effectiveness of the proposed method.


Document Image Analysis Handwritten Documents Skew Estimation Mixture Models EM 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aradhya, V.N.M., Naveena, C.: Text line segmentation of unconstrained handwritten kannada script. In: Proceedings of ACM International Conference on Communication, Computing & Security (ICCCS), Rourkela, India (2011) (accepted for publication)Google Scholar
  2. 2.
    Basu, S., Chaudhuri, C., Kundu, M., Narsipuri, M., Basu, D.K.: Skew angle correction and line extraction from unconstrained handwritten bengali text. In: Fifth International Conference on Advances in Pattern Recognition 2003, pp. 271–274 (2003)Google Scholar
  3. 3.
    Kapoor, R., Bagai, D., Kamal, T.S.: Skew angle detection of a cursive handwritten devanagari script character image. Journal of Indian Institute of Science 82, 161–175 (2002)Google Scholar
  4. 4.
    Kavallieratou, E., Fakotakis, N., Kokkinakis, G.: Skew angle estimation for printed and handwritten documents using the wignerville distribution. Image and Vision Computing 20, 813–824 (2002)CrossRefGoogle Scholar
  5. 5.
    Lu, Y., Tan, C.L.: A nearest neighbor chain based approach to skew estimation in document images. Pattern Recognition Letters 24, 2315–2323 (2003)CrossRefGoogle Scholar
  6. 6.
    Pavlidis, T., Zhou, J.: Page segmentation by white streams. In: Proceedings of 1st International Conference on Document Analysis and Recognition, pp. 945–953 (1991)Google Scholar
  7. 7.
    Roy, A., Bhowmik, T.K., Parui, S.K., Roy, U.: A novel approach to skew detection and character segmentation for handwritten bangla words. In: Proceedings of the Digital Imaging Computing: Techniques and Applications (DICTA 2005), p. 30 (2005)Google Scholar
  8. 8.
    Srihari, S.N., Govindaraju, V.: Analysis of Textual Images using the Hough Transform. Machine Vision and Applications 2, 141–153 (1989)CrossRefGoogle Scholar
  9. 9.
    Su, T.H., Zhang, T.W., Huang, H.J., Zhou, Y.: Skew detection for chinese handwriting by horizontal stroke histogram. In: Proceedings of Intl Conf. on Document Analysis and Recognition, pp. 899–903 (2007)Google Scholar
  10. 10.
    Aradhya, V.N.M, Rao, A., Kumar, G.H.: Language independent skew estimation technique based on gaussian mixture models: A case study on south indian scripts. In: International Conference on Pattern Recognition and Machine Intelligence (PReMI), pp. 487–493 (2007)Google Scholar
  11. 11.
    Yan, H.: Skew correction of document images using interline cross-correlation. Computer Vision, Graphics, and Image Processing 55, 538–543 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • V. N. Manjunath Aradhya
    • 1
  • C. Naveena
    • 1
  • S. K. Niranjan
    • 2
  1. 1.Dept. of Information Science and EnggDayananda Sagar College of EngineeringBangaloreIndia
  2. 2.Dept. of ISEMaharaja Institute of TechnologyMysoreIndia

Personalised recommendations