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Reseach Article

Writer Identification based on offline Handwritten Document Images in Kannada language using Empirical Mode Decomposition Method

by Karunakara K, Dr. B.P.Mallikarjunaswamy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 30 - Number 6
Year of Publication: 2011
Authors: Karunakara K, Dr. B.P.Mallikarjunaswamy
10.5120/3644-5090

Karunakara K, Dr. B.P.Mallikarjunaswamy . Writer Identification based on offline Handwritten Document Images in Kannada language using Empirical Mode Decomposition Method. International Journal of Computer Applications. 30, 6 ( September 2011), 31-36. DOI=10.5120/3644-5090

@article{ 10.5120/3644-5090,
author = { Karunakara K, Dr. B.P.Mallikarjunaswamy },
title = { Writer Identification based on offline Handwritten Document Images in Kannada language using Empirical Mode Decomposition Method },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 30 },
number = { 6 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume30/number6/3644-5090/ },
doi = { 10.5120/3644-5090 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:16:18.793405+05:30
%A Karunakara K
%A Dr. B.P.Mallikarjunaswamy
%T Writer Identification based on offline Handwritten Document Images in Kannada language using Empirical Mode Decomposition Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 30
%N 6
%P 31-36
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

New challenges in computer applications such as web searching, multimedia data retrieval, data mining, face recognition and handwriting recognition has attracted many researchers. Handwriting identification is one of the important research topics in the area of Pattern recognition and image processing. Many methods have been reported for identification of writer based on handwriting. In this paper, we propose a novel approach to identify the writer based on Kannada language using empirical mode decomposition (EMD). We have made an attempt to identify the writer with an assumptions that text is fixed. As each writer, handwriting visually differs from one another, each writer’s handwriting may be regarded as a different texture. These textures are taken as a unique characteristic to identify the writers. These textures are decomposed using EMD, which in turn generates series of intrinsic mode functions. The first four IMFs are considered for our purpose. Thus each handwritten image forms a 4-dimensional vector and is called Writer features. These features are stored for identification of test writer. The k-NN classifier is used to identify the test writer. The proposed method has been tested on a stored features containing handwriting features of 50 writers including machine printed one. Experiment result prove the robustness and flexibility of our approach. With this new approach encouraging experimental results have been obtained.

References
  1. A.K. Jain, Fellow, IEEE, R.P.W. Duin, and J. Mao, Senior Member, IEEE,”Statistical Pattern Recognition: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22 (1), January 2000.
  2. Plamond and G Lorette, “Automatic Signature Verification and Writer Identification – The state of the art”, Pattern Recognition,1989,Vol.22,No.2,pp107-131.
  3. A.K. Jain, R. Bolle, S. Pankanti (eds.). Biometrics. Personal Identification in Networked Society, Kluwer Academic. 1999. 679.
  4. A.K. Jain, L. Hong, S. Pankanti. Biometrics identification. Comm. ACM 43(2), pp. 91 – 98. 2000. 679.
  5. G.R. Ball and S.N. Srihari: Semi-Supervised Learning for Handwriting Recognition. ICDAR 2009: 26-30.
  6. D.Bayly, M. Castro, A. Arakala, J. Jeffers and K. Horadam, “Fractional Biometrics: Safeguarding Privacy in Biometric Applications”, Int. J. Inf. Secur. (2010) 9:69–82, Springer, Melbourne, Australia.
  7. C-Y. Low, A.B.J. Teoh and T. Connie: Fusion of LSB and DWT Biometric Watermarking Using Offline Handwritten Signature for Copyright Protection. ICB 2009: 786-795.
  8. E. Marcu, “Method of Combining the Degrees of Similarity in Handwritten Signature Authentication Using Neural Networks”, Research and Development in Intelligent, Springer-Verlag London, 2010, p. 481.
  9. T. Bourlai and J. Kittler, “On Design and Optimisation of Face Verification Systems that are Smart –Card Based” Machine Vision and Applications,February 11, 2009.
  10. E.Norouzi, M.N. Ahmadabadi and B.N. Araabi, “Attention control with reinforcement learning for face recognition under partial occlusion”, Machine Vision and Applications, Springer-Verlag 2010.
  11. Imran Siddiqe and Nicole Vincent.” Combining Local and Global features for Writer recognition”, ICHFR 2008.
  12. Alessandro Vinciarelli , Samy BengioWriter adaptation techniques in HMM based Off-Line Cursive Script Recognition Pattern Recognition Letters 23 (2002) 905–916.
  13. Bangy Li and Tieniu Tan, Online Text-independent Writer Identification Based on Temporal Sequence and Shape Codes 2009 10th International Conference on Document Analysis and Recognition.
  14. H.E.S Said, G.S. Peake, T. N. Tan, and K. D. Baker, Writer Identification from Non-uniformly Skewed Handwriting Images, British mission vision conference.
  15. Bulacu, M., Schomaker, L. and Vuurpijl, L.: 2003, Writer identification using edge-based directional features, Proc. of 7th Int. Conf. on Document Analysis and Recognition (ICDAR 2003), Vol. II, IEEE Computer Society, Edinburgh, Scotland, pp. 937–941.
  16. B.Li, Z.Sun, and T.N.Tan. “Hierarchical Shape Primitive Features for Online Text-independent Writer Identification”, Proc. of 2th ICB, pages 201–210, 2007.
  17. Mohamed Nidhal Abdi and Maher Khemakhem, Off-Line Text-Independent Arabic Writer Identification using Contour-Based Features, International Journal of Signal and Image Processing (Vol.1-2010/Iss.1) Abdi et Khemakhem / Off-Line Text-Independent Arabic Writer Identification … / pp. 4-11.
  18. J. Hochberg, K. Bowers, M. Cannon, P. Kelly, Script and language identification for handwritten document images, Int. J. Document Analysis and Recognition 2 (2-3) (1999) 45–52.
  19. S. Aksoy, 2010. Introduction to Pattern Recognition. Lecture Notes Comput. Sci., CS 551, Spring 2010.
  20. Amir Atapour Abarghouei, Afshin Ghanizadeh & Siti Mariyam Shamsuddin, Advances of Soft Computing in Edge Detection, International Journal of Advances in Soft Computing and Its Applications, Vol. 1, No. 2 (2009), 162 -199.
  21. N.E.Huang, Z.Shen, S.R.Long, et al. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” Proc. Roy. Soc, London. A, vol.454, pp.903-995, 1998.
  22. Norden E. Huang and Zhaohua Wu, An Adaptive Data Analysis Method for nonlinear and Nonstationary Time Series:The Empirical Mode Decomposition and Hilbert Spectral Analysis. www.deas.harvard.edu/climate/pdf/Zhaohua.pdf.
  23. G.S Peake and T. N.Tan, “Script and Language identification from Document Images”, Proc.BMVC’97, Essex,UK,Sept.97,Vol.2,PP.610-619.
  24. N. Arica and F.T. Yarman-Vural, ''An Overview of character recognition focused on off-line handwriting'', IEEE Transactions on System.Man.Cybernetics-Part C: Applications and Reviews, vol. 31, no. 2, pp. 216-233, 2001.
  25. V-C. Juan, A-C. Carlos, “Font Recognition by Invariant Moments of Global Textures”, In Proceedings of International Workshop VLBV05 (Very Low Bit-Rate Video-Coding 2005). 15-16 September 2005. Sardinia, Italy.
  26. Zhaohua Wu and Norden E. Huang, Ensemble Empirical Mode Decomposition: A Noise Assisted Data Analysis Method, Advances in Adaptive Data Analysis, World Scientific Publishing Company, Vol. 1, No. 1 (2008) 1–41.
  27. M. Bulacu and L.Schomaker, “Combining Multiple Features for Text- Independent Writer Identification and Verification”, Proc. of 10th Int’l Workshop on Frontiers in Handwriting Recognition (IWFHR 2006), 23-26 October, La Baule, France.
  28. S. K. Chan, C. Viard-Gaudin, and Y. H. Tay, "Online Writer Identification using Character Prototypes Distributions," in Proceedings of SPIE – The International Society for Optical Engineering, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Writer identification Empirical mode decomposition (EMD) Feature extraction Hilbert-Huang transforms Kannada Language k-Nearest Neighbor classifier