Advertisement

A Robust Scheme for Extraction of Text Lines from Handwritten Documents

  • Barun BiswasEmail author
  • Ujjwal Bhattacharya
  • Bidyut B. Chaudhuri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)

Abstract

Considering the vast collection of handwritten documents in various archives, research studies for their automatic processing have major impact in the society. Line segmentation from images of such documents is a crucial step. The problem is more difficult for documents of major Indian scripts such as Bangla because a large number of its characters have either ascender or descender or both and the majority of its writers are accustomed in extremely cursive handwriting. In this article, we describe a novel strip based text line segmentation method for handwritten documents of Bangla. Moreover, the proposed method has been found to perform efficiently on English and Devanagari handwritten documents. We conducted extensive experimentations and its results show the robustness of the proposed approach on multiple scripts.

Keywords

Handwritten document analysis Line segmentation Piecewise projection profile Connected component 

References

  1. 1.
    Mullick, K., Banerjee, S., and Bhattecharya, U.: An Efficient Line Segmentation Approach for Handwritten Bangla Document Image. Eighth International Conference on Advences in pattern Recognition (ICAPR), 1–6 (2015)Google Scholar
  2. 2.
    Alaei, A., Pal, U., and Nagabhushan, P.: A New Scheme for Unconstrained Handwritten Text-Line Segmentation. Pattern Recognition. 44(4), 917–928, (2011)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Papavassiliou, V., Stafylakis, T., Katsouros, V., Carayannis, G.: Handwritten document image segmentation into text lines and words. Pattern Recognition. 147, 369–377 (2010)CrossRefzbMATHGoogle Scholar
  4. 4.
    Shi, Z., Seltur, S., and Govindaraju, V.: A Steerable Directional Local Profile Technique for Extraction of Handwritten Arabic Text Lines. Proceedings of 10th International Conference on Document Analysis and Recognition, 176–180, (2009)Google Scholar
  5. 5.
    Louloudis, G., Gatos, B., and Halatsis, C: Text Line and Word Segmentation of Handwritten Documents. Pattern Recognition, 42(12):3169–3183, (2009)Google Scholar
  6. 6.
    Stamatopoulos, N., Gatos, B., Louloudis, G, Pal, U., Alaei, A.: ICDAR 2013 Handwritten Segmentation Contest. 12th International Conference on Document Analysis and Recognition, 14021–1406 (2013)Google Scholar
  7. 7.
    Likforman-Sulem, L., Zahour, A., and Taconet, B.: Text Line Segmentation of Historical Documents: a Survey. International Journal of Document Analysis and Recognition: 123–138, (2007)Google Scholar
  8. 8.
    Antonacopoulos, A., Karatzas, D.: Document Image analysis for World War II personal records, International Workshop on Document Image Analysis for Libraries. DIAL, 336–341 (2004)Google Scholar
  9. 9.
    Li, y., Zheng, Y., Doermann, D., and Jaeger, S.: A new algorithm for detecting text line in handwritten documents. International Workshop on Frontiers in Handwriting Recognition, 35–40 (2006)Google Scholar
  10. 10.
    Louloudis, G. Gatos, B., Pratikakis, I., Halatsis, K., Alaei, A.: A Block Based Hough Transform Mapping for Text Line Detection in Handwritten Documents. Proceedings of the Tenth International Workshop on Frontiers in Handwriting Recognition, 515–520 (2006)Google Scholar
  11. 11.
    Tsuruoka, S., Adachi, Y., and Yoshikawa, T.: Segmentation of a Text-Line for a Handwritten Unconstrained Document Using Thinning Algorithm, Proceedings of the 7th International Workshop on Frontiers in Handwriting Recognition:505–510, (2000)Google Scholar
  12. 12.
    Luthy, F., Varga, T., and Bunke, H.,: Using Hidden Markov Models as a Tool for Handwritten Text Line Segmentation. Ninth International Conference on Document Analysis and Recognition. 9, 630–632 (2007)Google Scholar
  13. 13.
    Lie, Y., Zheng, Y.: Script-Independent Text Line Segmentation in Freestyle Handwritten Documents. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(8), 1313–1329 (2008)CrossRefGoogle Scholar
  14. 14.
    Yin, F., Liu, C: A Variational Bayes Method for Handwritten Text Line Segmentation. International Conference on Document Analysis and Recognition. 10, 436–440 (2009)Google Scholar
  15. 15.
    Brodic, D., and Milivojevic, Z.: Text Line Segmentation by Adapted Water Flow Algorithm. Symposium on Neural Network Applications in Electrical Engineering. 10, 225–229 (2010)CrossRefGoogle Scholar
  16. 16.
    Dinh, T. N., Park, J., Lee, G.: Voting Based Text Line Segmentation in Handwritten Document Images. International Conference on Computer and Information Technology. 10, 529–535 (2010)Google Scholar
  17. 17.
    Biswas, B., Bhattacharya, U., and Chaudhuri, B.B.: A Global-to-Local Approach to Binarization of Degraded Document Images. 22nd International Conference on Pattern Recognition, 3008–3013 (2014)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Barun Biswas
    • 1
    Email author
  • Ujjwal Bhattacharya
    • 1
  • Bidyut B. Chaudhuri
    • 1
  1. 1.Computer Vision and Pattern Recognition UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations