Analysis of Segmentation Methods on Isolated Balinese Characters from Palm Leaf Manuscripts

  • Deepak Kumar
  • K. Vatsala
  • Sushmitha Pattanashetty
  • S. Sandhya
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)


Segmentation of an image is a complex process when image has to be divided into constituent and meaningful parts. In case of a clean image, the segmentation process is an easy task due to distinct foreground and background within the image. Whereas, the images captured or scanned from palm leaf manuscripts, the segmentation process is more complex and complicated. In this paper, we have used the individual characters from the palm leaf manuscript for segmentation. We have chosen multiple segmentation algorithms to perform segmentation of isolated Balinese characters. We evaluate the performance of all the algorithms on AMADI_Lontarset dataset. From our analysis, we observe that a global thresholding approach provides good segmentation on the set of analyzed images over the local thresholding approach.


Palm leaf manuscript Isolated character segmentation Segmentation Balinese script Performance evaluation 


  1. 1.
    Shi, Z., Setlur, S., Govindaraju, V.: Digital enhancement of palm leaf manuscript images using normalization techniques. In: Proceedings of the 5th International Conference on Knowledge-Based Computer Systems, Hyderabad, India, 19–22 December 2004Google Scholar
  2. 2.
    Chamchong, R., Fung, C.C.: Optimal selection of binarization techniques for the processing of ancient palm leaf manuscripts. IEEE (2010)Google Scholar
  3. 3.
  4. 4.
    Vijaya Lakshmi, T.R., Sastry, P.N., Rajnikanth, T.V.: A novel 3D approach to recognize Telugu palm leaf text. Eng. Sci. Technol. Int. J. 20(1), 143–150 (2017)CrossRefGoogle Scholar
  5. 5.
    Burie, J.-C., Coustaty, M., Hadi, S., Kesiman, M.W.A., Ogier, J.-M., Paulus, E., Sok, K., Sunarya, I.M.G., Valy, D.: ICFHR2016 competition on the analysis of handwritten text in images of balinese palm leaf manuscripts. In: 15th International Conference on Frontiers in Handwriting Recognition, pp. 596–601, Shenzhen, China (2016)Google Scholar
  6. 6.
    Kumar, D., Ramakrishnan, A.G.: Power-law transformation for enhanced recognition of born-digital word images. In: Proceedings of the 9th SPCOM (2012)Google Scholar
  7. 7.
    Kumar, D., Anil Prasad, M.N., Ramakrishnan, A.G.: NESP: nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images. In: Proceedings 20th DRR (2013)Google Scholar
  8. 8.
    Kumar, D., Anil Prasad, M.N., Ramakrishnan, A.G.: MAPS: midline analysis and propagation of segmentation. In: Proceedings 8th ICVGIP (2012)Google Scholar
  9. 9.
    Otsu, N.: A Thresholding selection method from gray-level histogram. IEEE Trans. SMC 9, 62–66 (1979)Google Scholar
  10. 10.
    Kumar, D., Anil Prasad, M.N., Ramakrishnan, A.G.: Evaluation of document binarization using eigen value decomposition. In: Proceedings of the 20th DRR (2013)Google Scholar
  11. 11.
    Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture threshold using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29, 273–285 (1985)CrossRefGoogle Scholar
  12. 12.
    Kittler, J., Illingworth, J., Föglein, J.: Threshold selection based on a simple image statistic. Comput. Vis. Graph. Image Process. 30, 125–147 (1985)CrossRefGoogle Scholar
  13. 13.
    Niblack, W.: An Introduction to Digital Image Processing. Prentice Hall, Englewood Cliffs (1986)Google Scholar
  14. 14.
    Sauvola, J.J., Pietaikinen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000)CrossRefGoogle Scholar
  15. 15.
    Su, B., Lu, S., Tan, C.L.: Binarization of historical document images using the local maximum and minimum. In: Proceedings of the 9th International Workshop on Document Analysis Systems (DAS 2010), pp. 159–166 (2010)Google Scholar
  16. 16.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2001)zbMATHGoogle Scholar
  17. 17.
    Trier, Ø.D., Taxt, T.: Evaluation of binarization methods for document images. IEEE Trans. PAMI 17, 312–315 (1995)CrossRefGoogle Scholar
  18. 18.
    Trier, Ø.D., Jain, A.K.: Goal-directed evaluation of binarization methods. IEEE Trans. PAMI 17, 1191–1201 (1995)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Dayananda Sagar Academy of Technology and Management (DSATM)BengaluruIndia

Personalised recommendations