Improved Zoning and Cropping Techniques Facilitating Segmentation

  • Monika KohliEmail author
  • Satish Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)


In the advent of digital computers and era where work force is shifted to be inclined on robotic process, Optical Character Recognition (OCR) has immense potentials to ease some these processes. Segmentation is one of the pre-processing phases- the pivotal essence of the process where lingual scripts and their characteristics vary to a much larger extent. This paper focuses on techniques which facilitates segmentation in Devanagari script (Hindi) for offline handwritten words i.e. Headline detection in handwritten word images of Hindi for extracting upper and middle zone characters and cropping. Experiments are performed on the handwritten legal amount words ICDAR database [1] on 106 words by 80 writers and on Self created touching character database on 106 words by 15 writers. The proposed zoning technique i.e. CPT (Continuous pixel technique) and cropping techniques is implemented on 10070 and 530 legal amount words with 98.89% accuracy and 80.94% respectively.


Handwritten data Optical character recognition (OCR) Segmentation Zoning 



I am thankful to Jayadevan R., ICDAR for support and providing word database of offline handwritten words database in Hindi.


  1. 1.
    Jayadevan, R., Kolhe, S.R., Patil, P.M., Pal, U.: Database development and recognition of handwritten Devanagari legal amount words. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 304–308 (2011)Google Scholar
  2. 2.
    Kumar, S.: An analysis of irregularities in Devanagari script writing—a machine recognition perspective. Int. J. Comput. Sci. Eng. 2, 274–279 (2010)Google Scholar
  3. 3.
    Choudhary, A., Rishi, R., Ahlawat, S.: New character segmentation approach for off-line cursive handwritten words. Procedia Comput. Sci. 17, 88–95 (2013)Google Scholar
  4. 4.
    Elnagar, A., Alhajj, R.: Segmentation of connected handwritten numeral strings. Pattern Recognit. 36, 625–634 (2003)Google Scholar
  5. 5.
    Jayarathna, U.K.S., Bandara, G.E.M.D.C.: A junction based segmentation algorithm for offline handwritten connected character segmentation. In: International Conference on Computational Intelligence for Modelling, Control and Automation, 2006 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, p. 147 (2006)Google Scholar
  6. 6.
    Kim, K.K., Kim, J.H., Suen, C.Y.: Segmentation-based recognition of handwritten touching pairs of digits using structural features. Pattern Recognit. Lett. 23, 13–24 (2002)Google Scholar
  7. 7.
    Saba, T., Sulong, G., Rehman, A.: Non-linear segmentation of touched roman characters based on genetic algorithm. Int. J. Comput. Sci. Eng. 2, 2167–2172 (2010)Google Scholar
  8. 8.
    Reddy, L.P., Babu, T.R., Rao, N.V., Babu, B.R.: Touching syllable segmentation using split profile algorithm. Int. J. Comput. Sci. Issues (IJCSI) 7(3), 1–10 (2010)Google Scholar
  9. 9.
    Bag, S., Bhowmick, P., Harit, G., Biswas, A.: Character segmentation of handwritten Bangla text by vertex characterization of isothetic covers. In: 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 21–24 (2011)Google Scholar
  10. 10.
    Venkatesh, M., Majjagi, V., Vijayasenan, D.: Implicit segmentation of Kannada characters in offline handwriting recognition using hidden Markov models. Implicit arXiv1410.4341, pp. 1–6 (2014)
  11. 11.
    Bag, A.S., Krishna: Character segmentation of Hindi unconstrained handwritten words. In: International Workshop on Combinatorial Image Analysis, vol. 9448, pp. 247–260. Springer, Cham (2015)Google Scholar
  12. 12.
    Garg, N.K., Kaur, L., Jindal, M.K.: The hazards in segmentation of handwritten Hindi Text. Int. J. Comput. Appl. 29, 30–34 (2011)Google Scholar
  13. 13.
    Palakollu, S., Rani, R.: Handwritten Hindi text segmentation techniques for lines and characters. In: Proceedings of the World Congress on Engineering and Computer Science (2012)Google Scholar
  14. 14.
    Garg, N.K.: A new method for line segmentation of handwritten Hindi text key words. In: Seventh International Conference on Information Technology, pp. 392–397 (2010)Google Scholar
  15. 15.
    Hanmandlu, M.B.L., Agrawal, P.: Segmentation of handwritten Hindi text: a structural approach. Int. J. Comput. Proc. Languages 22(01), 1–20 (2001)Google Scholar
  16. 16.
    Bhujade, M.V.G., Meshram, M.C.M.: A technique for segmentation of handwritten Hindi text. Int. J. Eng. Res. Technol. 3, 1491–1495 (2014)Google Scholar
  17. 17.
    Ramteke, A.S., Rane, M.E.: Offline handwritten devanagari script segmentation. Int. J. Sci. Res. 1, 142–145 (2012)Google Scholar
  18. 18.
    Garain, U., Chaudhuri, B.B.: Segmentation of touching and fused Devanagari characters. Pattern Recognit 32, 449–459 (2002)Google Scholar
  19. 19.
    Bansal, V., Sinha, R.M.K.: Segmentation of touching and fused Devanagari characters. Pattern Recognit. 35, 875–893 (2002)Google Scholar
  20. 20.
    Kumar, M.: Segmentation of isolated and touching characters in offline handwritten Gurmukhi script recognition. Int. J. Inf. Technol. Comput. Sci. 2, 58–63 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and ApplicationsPanjab UniversityChandigarhIndia
  2. 2.Department of Computer ApplicationsPanjab University, SSG Regional CentreHoshiarpurIndia

Personalised recommendations