Text Line Segmentation of Unconstrained Handwritten Kannada Historical Script Documents

  • H. S. VishwasEmail author
  • Bindu A. Thomas
  • C. Naveena
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 14)


Text line segmentation of historical document is a challenging task in the field of document image analysis due to the presence of narrow spacing between the text lines, overlapping of characters and touching characters. Initially, the document image is preprocessed by means of binarization and thinning. Components are then labeled with the help of connected component labeling method. Finally text lines are localized with the help of projection profile and search for the foreground pixel in the neighborhood to assign characters to their respective text lines. Experimentation is carried on the historical Hoysala Kannada scripts and encouraging results are obtained.


Historical documents Text line segmentation Connected components Projection profile 



The authors would like to thank Prof. Manjunath M., Head of the department, Prasararanga, University of Mysore for helping us in creating the data base and analyzing them.


  1. 1.
    L-Sulem L, Zahour A, Taconet B (2007) Text line segmentation of historical documents: a survey. Int J Doc Anal Recogn (IJDAR) 9(2):123–138Google Scholar
  2. 2.
    Aradhya VNM, Naveena C (2011) Text line segmentation of unconstrained handwritten Kannada script. In: Proceedings of the 2011 international conference on communication, computing and security, ICCCS’11, pp 231–234Google Scholar
  3. 3.
    Boussellaa W, Zahour A, Elabed H, Benabdelhafid A, Alimi AM (2010) Unsupervised block covering analysis for text-line segmentation of arabic ancient handwritten document images. In: Proceedings of 20th international conference on pattern recognition (ICPR), pp 1929–1932Google Scholar
  4. 4.
    Alaei A, Nagabhushan P, Pal U (2011) Piece-wise painting technique for line segmentation of unconstrained handwritten text: a specific study with persian text documents. Pattern Anal Appl 14(4):381–394MathSciNetCrossRefGoogle Scholar
  5. 5.
    Louloudis G, Gatos B, Pratikakis I, Halatsis K (2006) A block-based hough transform mapping for text line detection in handwritten documents. In: Proceedings of tenth international workshop on frontiers in handwriting recognitionGoogle Scholar
  6. 6.
    Roy PP, Pal U, Lladós J (2008) Morphology based handwritten line segmentation using foreground and background information. In: Proceedings of international conference on frontiers in handwriting recognition, pp 241–246Google Scholar
  7. 7.
    Shi Z, Setlur S, Govindaraju V (2005) Text extraction from gray scale historical document images using adaptive local connectivity map. In: Proceedings of eighth international conference on document analysis and recognition (ICDAR’05), pp 794–798Google Scholar
  8. 8.
    Kennard DJ, Barrett WA (2006) Separating lines of text in free-form handwritten historical documents. In: Proceedings of the second international conference on document image analysis for libraries, DIAL, pp 12–23Google Scholar
  9. 9.
    Surinta O, Holtkamp M, Karabaa F, Van Oosten J-P, Schomaker L, Wiering M (2014) A path planning for line segmentation of handwritten documents. In: Proceedings of 14th international conference on frontiers in handwriting recognition (ICFHR), pp 175–180Google Scholar
  10. 10.
    Liwicki M, Indermuhle E, Bunke H (2007) On-line handwritten text line detection using dynamic programming. In: Proceedings of ninth international conference on document analysis and recognition (ICDAR), vol 1. IEEE, pp 447–451Google Scholar
  11. 11.
    Basu S, Chaudhuri C, Kundu M, Nasipuri M, Basu DK (2007) Text line extraction from multi-skewed handwritten documents. Pattern Recogn 40(6):1825–1839CrossRefzbMATHGoogle Scholar
  12. 12.
    Yin F, Liu C-L (2009) A variational Bayes method for handwritten text line segmentation. In: Proceedings of 10th international conference on document analysis and recognition, pp 436–440Google Scholar
  13. 13.
    Yin F, Liu C-L (2009) Handwritten chinese text line segmentation by clustering with distance metric learning. Pattern Recogn 42(12):3146–3157CrossRefzbMATHGoogle Scholar
  14. 14.
    Sauvola J, Pietikäinen M (2000) Adaptive document image binarization. Pattern Recogn 33(2):225–236CrossRefGoogle Scholar
  15. 15.
    Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285–296):23–27Google Scholar
  16. 16.
    Zhang T, Suen CY (1984) A fast parallel algorithm for thinning digital patterns. Commun ACM 27(3):236–239CrossRefGoogle Scholar
  17. 17.
    Manjunath MG, Devarajaswamy GK “Kannada Lipi Vikasa”, Yuvasadhane, BengaluruGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringVidya Vikas Institute of Engineering and TechnologyMysoreIndia
  2. 2.Department of Computer Science and EngineeringSJB Institute of TechnologyBengaluruIndia

Personalised recommendations