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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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General Research Article

SVM and HMM Classifier Combination Based Approach for Online Handwritten Indic Character Recognition

Author(s): Rajib Ghosh and Prabhat Kumar*

Volume 13, Issue 2, 2020

Page: [200 - 214] Pages: 15

DOI: 10.2174/2213275912666181127124711

Price: $65

Abstract

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts.

Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil.

Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters.

Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard.

Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

Keywords: Online handwriting, character recognition, indic scripts, zone-wise feature extraction, SVM, HMM combination

Graphical Abstract

[1]
S. Jaeger, S. Manke, J. Reichert, and A. Waibel, "Online handwriting recognition: The NPen++ recognizer", Int. J. Doc. Anal. Recognit., vol. 3, no. 3, pp. 169-180, 2001.
[2]
A. Yuan, G. Bai, P. Yang, Y. Guo, and X. Zhao, "Handwritten English word recognition based on convolutional neural networks", Proceedings of the 13th International Conference on Frontiers in Handwriting Recognition. Bari, Italy, 2012, pp. 207-2012
[3]
S.D. Connell, and A.K. Jain, "Template-based on-line character recognition", Pattern Recognit.. Vol. 34, 2001, pp. 1-14.
[4]
J. Hu, S.G. Lim, and M.K. Brown, "Writer independent on-line handwriting recognition using an HMM approach", Pattern Recognit.. Vol. 33, No. 1, 2000, pp. 133-147
[5]
Z. Yao, X. Ding, and C. Liu, "On-line handwritten Chinese word recognition based on lexicon", Proceedings of the 18th International Conference on Pattern Recognition. Hong Kong, 2006, pp. 320-323
[6]
X. Zhou, J. Yu, C. Liu, T. Nagasaki, and K. Marukawa, "online handwritten Japanese character string recognition incorporating geometric context", Proceedings of the 9th International Conference on Document Analysis and Recognition.Curitiba, Brazil 2007, pp.48-52.M
[7]
B. Zhu, and M. Nakagawa, "Online handwritten Japanese characters recognition using a MRF model with parameter optimization by CRF", Proceedings of the 10th International Conference on Document Analysis and Recognition.Beijing, China 2011, pp. 603-607
[8]
M. Nakai, N. Akira, H. Shimodaira, and S. Sagayama, "Substroke approach to hmm-based on- line Kanji handwriting recognition", Proceedings of the 6th International Conference on Document Analysis and Recognition.Seattle, USA 2001, pp. 491-495
[9]
H.S.M. Nakai, and S. Sagayama, "Generation of hierarchical dictionary for stroke-order free Kanji handwriting recognition based on substroke HMM", Proceedings of the 7th International Conference on Document Analysis and Recognition.Edinburgh, Scotland 2003, pp. 514-518
[10]
N. Joshi, G. Sita, A.G. Ramakrishnan, and S. Madhvanath, "Comparison of elastic matching algorithms for online Tamil handwritten character recognition", Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition.Tokyo, Japan 2004, pp. 444-449
[11]
R. Niels, and L. Vuurpijl, "Dynamic time warping applied to Tamil character recognition", Proceedings of the 8th International Conference on Document Analysis and Recognition.Seoul, Korea 2005, pp. 730-734
[12]
C.S. Sundaresan, and S.S. Keerthi, "A study of representations for pen based handwriting recognition of Tamil characters", Proceedings of the 5th International Conference on Document Analysis and Recognition.Bangalore, India 1999, pp. 422-425.
[13]
V. Deepu, S. Madhvanath, and A.G. Ramakrishnan, "Principal component analysis for online handwritten character recognition", Proceedings of the 17th International Conference on Pattern Recognition.Cambridge, United Kingdom 2004, pp. 327-330
[14]
A.H. Toselli, M. Pastor, and E. Vidal, "On-Line handwriting recognition system for tamil handwritten characters", In: Pattern Recognition and Image Analysis., Springer: Berlin, Heidelberg, July 2007, pp. 370-377.
[15]
S. Sundaram, and A.G. Ramakrishnan, "A novel hierarchical classification scheme for online Tamil character recognition", Proceedings of the 9th International Conference on Document Analysis and Recognition.Curitiba, Brazil 2007, pp. 1218-1222.
[16]
T. Mondal, U. Bhattacharya, S.K. Parui, K. Das, and D. Mandalapu, "On-line handwriting recognition of Indian scripts-the first benchmark", Proceedings of the 12th International Conference on Frontiers in Handwriting Recognition.Kolkata, India 2010, pp.200-205.
[17]
H. Swethalakshmi, A. Jayaraman, V.S. Chakravarthy, and C.C. Sekhar, "Online handwritten character recognition of Devanagari and Telugu Characters using Support Vector Machines", Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition.La Baule, France 2006
[18]
S.D. Connell, R.M.K. Sinha, and A.K. Jain, "Recognition of unconstrained on-line Devanagari characters", Proceedings of the 15th International Conference on Pattern Recognition.Barcelona, Spain 2000, pp. 368-371
[19]
N. Joshi, G. Sita, A.G. Ramakrishnan, V. Deepu, and S. Madhvanath, "Machine recognition of online handwritten devanagari characters", Proceedings of the 8th International Conference on Document Analysis and Recognition. Seoul, Korea, 2005, pp. 1156-1160.
[20]
U. Garain, B.B. Chaudhuri, and T. Pal, "Online handwritten indian script recognition: a human motor function based framework", Proceedings of the 16th International Conference on Pattern Recognition.Quebec, Canada 2002, pp. 164-167.
[21]
R. Ghosh, and P.P. Roy, "Study of two zone based features for online Bengali and Devanagari character recognition", Proceedings of the 13th International Conference on Document Analysis and Recognition.Nancy, France 2015, pp. 401-405.
[22]
U. Bhattacharya, B.K. Gupta, and S.K. Parui, "Direction code based features for recognition of online handwritten characters of Bangla", Proceedings of the 9th International Conference on Document Analysis and Recognition.Curitiba, Brazil 2007, pp. 58-62.
[23]
S.K. Parui, K. Guin, U. Bhattacharya, and B.B. Chaudhuri, "Online handwritten Bangla character recognition using HMM", Proceedings of the 19th International Conference on Pattern Recognition.Florida, USA 2008, pp. 1-4.
[24]
C. Biswas, U. Bhattacharya, and S.K. Parui, "HMM based online handwritten Bangla character recognition using Dirichlet distributions", Proceedings of the 13th International Conference on Frontiers in Handwriting Recognition. Bari, Italy, 2012, pp. 598-603.
[25]
S. Sen, "R. Sarkar, K. Roy and N. Hori, “Recognize online handwritten Bangla characters using Hausdorff distance-based feature”", Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications.Bhubaneswar, India 2017, pp. 541-549
[26]
S. Sen, A. Bhattacharya, P. K. Singh, R. Sarkar, K. Roy, and D. Doermann, "Application of structural and topological features to recognize online handwritten Bangla characters", ACM Transact.Asian and Low-Res. Lang. Info. Proc.. Vol. 17, No. 3, 2018.
[27]
A. Bharath, and S. Madhvanath, "HMM-Based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts", IEEE Trans. Pattern Anal. Mach. Intell.. Vol. 34, No. 4, 2012, pp.670-682
[28]
P.P. Roy, A.K. Bhunia, A. Das, P. Dey, and U. Pal, "HMM-based Indic handwritten word recognition using zone segmentation", Pattern Recognit., vol. 60, pp. 1057-1075, 2016.
[29]
HP Labs India, Isolated handwritten Tamil character dataset.Available from:, http://lipitk.sourceforge.net/hpl- datasets.htm
[30]
C. Burges, "A tutorial on support vector machines for pattern recognition", Data Min. Knowl. Discov., vol. 2, pp. 1-43, 1998.
[31]
U. Pal, P.P. Roy, N. Tripathy, and J. Llados, "Multi-oriented Bangla and Devanagari text recognition", Pattern Recognit., vol. 43, pp. 4124-4136, 2010.
[32]
V.N. Vapnik, The nature of statistical learning theory., 1st ed Springer: Berlin, Heidelberg, 1995.
[33]
R. Ghosh, P.P. Roy, and P. Kumar, "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., vol. 9, no. 1, pp. 21-55, 2018.
[34]
S. Young, “The HTK Book”, Version 3.4.2006Cambridge University Engineering Department, .
[35]
Y. Kessentini, T. Burger, and T. Paquet, "A Dempster-Shafer theory based combination of handwriting recognition systems with multiple rejection strategies", Pattern Recognit., vol. 48, no. 2, pp. 534-544, 2015.
[36]
D. Dubois, H. Prade, and P. Smets, "New semantics for quantitative possibility theory", Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty.Toulouse, France 2001, pp. 410-421
[37]
K.H. Aparna, V. Subramanian, M. Kasirajan, G.V. Prakash, V.S. Chakravarthy, and S. Madhvanath, "Online handwriting recognition for Tamil", Proceedings of the 9th International Workshop on Fron- tiers in Handwriting Recognition.Tokyo, Japan 2004, pp. 438-443.
[38]
L. Prasanth, V.J. Babu, R.R. Sharma, G.V.P. Rao, and M. Dinesh, "Elastic matching of online handwritten Tamil and Telugu scripts using local features", Proceedings of the 9th International Conference on Document Analysis and Recognition.Curitiba, Brazil 2007, pp. 1028-1032.
[39]
A. Bharath, and S. Madhvanath, Online handwriting recognition for indic scripts.OCR for Indic Scripts: Document Recognition and Retrieval., Springer: Berlin, Heidelberg, 2008.
[40]
V.J. Babu, L. Prasanth, R.R. Sharma, G.V.P. Rao, and A. Bharath, "HMM-Based online handwriting recognition system for telugu symbols", Proceedings of the 9th International Conference on Document Analysis and Recognition.Curitiba, Brazil 2007, pp. 63-67.
[41]
N. Bhattacharya, and U. Pal, "Stroke segmentation and recognition from Bangla online handwritten text", Proceedings of the 13th International Conference on Frontiers in Handwriting Recognition. Bari, Italy, 2012, pp. 736-741.
[42]
A. Bharath, and S. Madhvanath, "HMM-Based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts", IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 670-682, 2012.
[43]
P.P. Roy, P. Dey, S. Roy, U. Pal, and F. Kimura, "A novel approach of Bangla handwritten text recognition using HMM", Proceedings of the 14th International Conference on Frontiers in Handwriting Recognition.Heraklion, Greece 2014, pp. 661-666.
[44]
B. Scholkopf, S. Kah-Kay, C.J.C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, "Comparing support vector machines with Gaussian kernels to radial basis function classifiers", IEEE Transact. Sign. Proc., vol. 45, no. 11, pp. 2758-2765, 1997.
[45]
J. Platt, Probabilistic outputs for support vector machines and comparison to regularized likelihood methods., Advan. Large Margin Class, pp. 40-61. 1999
[46]
L.R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition", Proc. IEEE. Vol. 77,No. 2, 1989, pp. 257-286
[47]
G. Shrivastava, A. Pandey, and K. Sharma, "Steganography and its technique: Technical overview", In: Proceedings of the 3rd International Conference on Trends in Information, Telecommunication and ComputingSpringer: New York, NY, . 2013, pp. 615-620
[48]
F. Biadsy, J. El-Sana, and N. Habash, "Online Arabic handwriting recognition using hidden Markov models", Proceedings of the 10th International Workshop on Frontiers in Hand-writing Recognition.La Baule, France 2006, pp. 85-90
[49]
F. Biadsy, R. Saabni, and J. El-Sana, "Segmentation-free online arabic handwriting recognition", Int. J.of Pattern Recognit. Artif. Intell.. Vol. 25, No. 7, 2011, pp. 1009-1033.
[50]
J.D. Powell, "Radial basis function approximations to polynomials", In: Numerical Analysis. D.F. Griffiths, G.A. Watson Eds.;, Longman Publishing Group: London, United Kingdom, 1988, pp. 223-241.
[51]
L. Rabiner, and B. Jnuang, Fundamentals of speech recognition., Prentice-Hall: Eaglewood Cliffs, New Jersey, 1993.
[52]
A. Kundu, "Handwritten word recognition using hidden Markov model", In: Handbook of Character Recognition and Document Image Analysis, World Scientific.H. Bunke, P. Wang, Eds.,, Singapore, 1997, pp. 157-182.
[53]
L.A. Zadeh, "A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination", Pattern Recognit.. 1986, Vol. 7, No. 2, pp. 85-90

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