Advertisement

Sādhanā

, 44:178 | Cite as

An approach based on classifier combination for online handwritten text and non-text classification in Devanagari script

  • Rajib GhoshEmail author
  • Saurav Shanu
  • Sugandha Ranjan
  • Khusboo Kumari
Article

Abstract

In this article, a method of analysing features of elliptical regions and combining outcomes of classifiers using Dempster–Shafer Theory (DST) is presented to classify online handwritten text and non-text data of any online handwritten document in the most popular Indic script—Devanagari. Although a few works exist in this regard in different non-Indic scripts, to our knowledge, no study is available to classify handwritten text and non-text document in online mode in any Indic script. The present method uses various structural and directional features analysed in elliptical regions to extract feature values from strokes of text and non-text data. The features are then studied separately in classification platforms based on Support Vector Machine (SVM) and Hidden Markov Model (HMM). The probabilistic outcomes of these two classification platforms are then combined using DST to improve the system performance. The efficiency of the present system has been measured on a self-generated dataset and it provides promising result.

Keywords

Online handwriting text/non-text classification SVM HMM classifier combination DST 

References

  1. 1.
    Delaye A and Lee K 2015 A flexible framework for online document segmentation by pairwise stroke distance learning. Pattern Recognit. 48: 1197–1210CrossRefGoogle Scholar
  2. 2.
    Delaye A and Liu C L 2014 Contextual text/non-text stroke classification in online handwritten notes with conditional random fields. Pattern Recognit. 47(3): 959–968CrossRefGoogle Scholar
  3. 3.
    Van Phan T and Nakagawa M 2014 Text/non-text classification in online handwritten documents with recurrent neural networks. In: Proceedings of the 14th International Conference on Frontiers in Handwriting Recognition, Heraklion, Greece. IEEE Press, pp. 23–28Google Scholar
  4. 4.
    Zhou X D and Liu C L 2007 Text/non-text ink stroke classification in Japanese handwriting based on Markov random fields. In: Proceedings of the 9th International Conference on Document Analysis and Recognition, Parana, Brazil. IEEE Press, pp. 377–381Google Scholar
  5. 5.
    Zhou X D, Wang D H and Liu C 2009 A robust approach to text line grouping in online handwritten Japanese documents. Pattern Recognit. 42(9): 2077–2088CrossRefGoogle Scholar
  6. 6.
    Liwicki M, Indermuhle E and Bunke H 2007 Online hand written text line detection using dynamic programming. In: Proceedings of the 9th International Conference on Document Analysis and Recognition, Parana, Brazil. IEEE Press, pp. 447–451Google Scholar
  7. 7.
    Ye M, Sutanto H, Raghupathy S, Li C and Shilman M 2005 Grouping text lines in free form handwritten notes. In: Proceedings of the 8th International Conference on Document Analysis and Recognition, Seoul, South Korea. IEEE Press, pp. 367–371Google Scholar
  8. 8.
    Blanchard J and Artieres T 2004 On-line handwritten documents segmentation. In: Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition, Tokyo, Japan. IEEE Press, pp. 148–153Google Scholar
  9. 9.
    Ghosh R, Vamsi C and Kumar P 2019 RNN based online handwritten word recognition in Devanagari and Bengali scripts using horizontal zoning. Pattern Recognit. 92: 203–218CrossRefGoogle Scholar
  10. 10.
    Mathew M, Jain M and Jawahar C V 2017 Benchmarking scene text recognition in Devanagari, Telugu and Malayalam. In: Proceedings of the 14th International Conference on Document Analysis and Recognition, Kyoto, Japan. IEEE Press, pp. 42–46Google Scholar
  11. 11.
    Feng G, Viard-Gaudin C and Sun Z 2009 Online hand-drawn electric circuit diagram recognition using 2D dynamic programming. Pattern Recognit. 42(12): 3215–3223CrossRefGoogle Scholar
  12. 12.
    Bresler M, VanPhan T, Prusa D, Nakagawa M and Hlavc V 2014 Recognition system for online sketched diagrams. In: Proceedings of the 14th International Conference on Frontiers in Handwriting Recognition, Heraklion, Greece. IEEE Press, pp. 563–568Google Scholar
  13. 13.
    Bharath A and Madhvanath S 2012 HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts. IEEE Trans. Pattern Anal. Mach. Intell. 34(4): 670–682CrossRefGoogle Scholar
  14. 14.
    Ghosh R, Roy P P and Kumar P 2018 Smart device authentication based on online handwritten script identification and word recognition in Indic scripts using zone-wise features. Int. J. Inf. Syst. Model. Des. 9(1): 21–55CrossRefGoogle Scholar
  15. 15.
    Burges C J C 1998 A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2): 121–167CrossRefGoogle Scholar
  16. 16.
    Pal U, Roy P P, Tripathy N and Llados J 2010 Multi-oriented Bangla and Devanagari text recognition. Pattern Recognit. 43: 4124–4136CrossRefGoogle Scholar
  17. 17.
    Vapnik V 1995 The Nature of Statistical Learning Theory, 2nd ed. New York: Springer, pp. 1–314CrossRefGoogle Scholar
  18. 18.
    Rabiner L R 1989 A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2): 257–286CrossRefGoogle Scholar
  19. 19.
    Ghosh R, Kumar P and Roy P P 2018 A Dempster–Shafer theory based classifier combination for online signature recognition and verification systems. Int. J. Mach. Learn. Cybern. https://doi.org/10.1007/s13042-018-0883-9, pp. 1–16

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology PatnaPatnaIndia

Personalised recommendations