Prediction of Writer Using Tamil Handwritten Document Image Based on Pooled Features
Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.
[1] Bulacu M and Schomaker L, “Text-independent writer identification and
verification using textural and allographic features,” IEEE Transactions
on Pattern Analysis and Machine Intelligence, pp.701–717, April 2007.
[2] Jagadeesh Kannan R, Prabhakar R, “A Comparative study of Optical
Character Recognition for Tamil Script”, European Journal of Scientific
Research,Vol.35,No.4(2009), pp.570-582.
[3] Wong Yee Leng & Siti Mariyam Shamsuddin, “Writer Identification for
Chinese Handwriting”, International Journal of Advances in Soft
Computing and Its Applications, Vol. 2, No. 2, July2010
[4] Thendral T, Vijaya MS, Karpagavalli S, “Analysis of Tamil Character
Writings and Identification of Writer Using Support Vector Machine”,
IEEE International Conference on Advanced Communication Control
and Computing Technologies (ICACCCT).
[5] Thendral T, Vijaya MS, Karpagavalli S, “Supervised Learning Approach
for Tamil Writer Identity Prediction Using Global and Local Features,
International Journal of Research in Engineering and Technology
(IJRET) Vol. 2, No. 5, 2013 ISSN 2277 – 4378, Page 204 – 208.
[6] Sargur N. Srihari Ph.D., Sung-Hyuk Cha Ph.D., Hina Arora M.E. and
Sangjik Lee M.S., “Individuality of Handwriting”, J Forensic Sci, July
2002, Vol. 47, No. 4 Paper ID JFS2001227_474.
[7] H. E. S. Said, G. S. Peake, T. N. Tan and K. D. Baker, “Writer
Identification from Non-uniformly Skewed Handwriting Images”,
British Machine Vision Conference.
[8] M. Blumenstein and X. Y. Liu, B. Verma, “A modified direction feature
for cursive character recognition”, IEEE International Joint Conference,
Vol. 4, 25-29 July 2004, Page: 2983-2987
[9] Thendral T, Vijaya MS, Karpagavalli S, “Discovering Tamil Writer
Identity Using Global and Local Features of Offline Handwritten Text”,
International Review on Computers and Software, Vol.8, N.9, ISSN
1828-6003,September 2013.
[10] Dunn D, Higgins W.E.,“Optimal Gabor filters for texture
segmentation”, IEEE Transactions on Image Processing, July
1995,ISSN:1057-7149, Page: 947-964.
[11] F. Shahabi M. Rahmati, “Comparison of Gabor-Based Features for
Writer Identification of Farsi/Arabic Handwriting”.
[12] Revathi S V and Vijaya M S, “Predicting the Identity of a Person using
Aggregated Features of Handwriting’, Advances in Image and Video
Processing, Volume 2 No 6, Dec (2014); pp: 23-33
[13] Zhenyu Heb, Xinge Youb, Yuan Yan Tanga, “Writer identification
using global wavelet-based features”, Elsevier publication,
Neurocomputing (2008), page: 1832–1841.
[14] Zhenyu He, Yuan Yan Tang, Xinge You, “A Contourlet -based method
for writer identification”, IEEE International Conference on Systems,
Man and Cybernetics,Vol.1, 10-12 Oct 2005, Page: 364-368.
[15] Marius Bulacu, Lambert Schomaker, Louis Vuurpijl. (2003), “Writer
Identification Using Edge-Based Directional Features”, Proceedings of
the Seventh International Conference on Document Analysis and
Recognition.
[16] Gadkari, Dhanashree, “Image Quality Analysis Using GLCM”, UCF
Electronic Theses and Dissertations, 2004-12-01.
[17] Saranya K, Vijaya M.S, “An interactive Tool for Writer Identification
based on Offline Text Dependent Approach”, International Journal of
Advanced Research in Artificial Intelligence, Vol. 2, No. 1, 2013. [18] CRAMMER Koby, Yoram Singer, “On the Algorithmic Implementation
of Multi-class Kernel-based Vector Machines”, Journal of Machine
Learning Research, MIT Press, Cambridge, Vol.2 Page 265-292.
[1] Bulacu M and Schomaker L, “Text-independent writer identification and
verification using textural and allographic features,” IEEE Transactions
on Pattern Analysis and Machine Intelligence, pp.701–717, April 2007.
[2] Jagadeesh Kannan R, Prabhakar R, “A Comparative study of Optical
Character Recognition for Tamil Script”, European Journal of Scientific
Research,Vol.35,No.4(2009), pp.570-582.
[3] Wong Yee Leng & Siti Mariyam Shamsuddin, “Writer Identification for
Chinese Handwriting”, International Journal of Advances in Soft
Computing and Its Applications, Vol. 2, No. 2, July2010
[4] Thendral T, Vijaya MS, Karpagavalli S, “Analysis of Tamil Character
Writings and Identification of Writer Using Support Vector Machine”,
IEEE International Conference on Advanced Communication Control
and Computing Technologies (ICACCCT).
[5] Thendral T, Vijaya MS, Karpagavalli S, “Supervised Learning Approach
for Tamil Writer Identity Prediction Using Global and Local Features,
International Journal of Research in Engineering and Technology
(IJRET) Vol. 2, No. 5, 2013 ISSN 2277 – 4378, Page 204 – 208.
[6] Sargur N. Srihari Ph.D., Sung-Hyuk Cha Ph.D., Hina Arora M.E. and
Sangjik Lee M.S., “Individuality of Handwriting”, J Forensic Sci, July
2002, Vol. 47, No. 4 Paper ID JFS2001227_474.
[7] H. E. S. Said, G. S. Peake, T. N. Tan and K. D. Baker, “Writer
Identification from Non-uniformly Skewed Handwriting Images”,
British Machine Vision Conference.
[8] M. Blumenstein and X. Y. Liu, B. Verma, “A modified direction feature
for cursive character recognition”, IEEE International Joint Conference,
Vol. 4, 25-29 July 2004, Page: 2983-2987
[9] Thendral T, Vijaya MS, Karpagavalli S, “Discovering Tamil Writer
Identity Using Global and Local Features of Offline Handwritten Text”,
International Review on Computers and Software, Vol.8, N.9, ISSN
1828-6003,September 2013.
[10] Dunn D, Higgins W.E.,“Optimal Gabor filters for texture
segmentation”, IEEE Transactions on Image Processing, July
1995,ISSN:1057-7149, Page: 947-964.
[11] F. Shahabi M. Rahmati, “Comparison of Gabor-Based Features for
Writer Identification of Farsi/Arabic Handwriting”.
[12] Revathi S V and Vijaya M S, “Predicting the Identity of a Person using
Aggregated Features of Handwriting’, Advances in Image and Video
Processing, Volume 2 No 6, Dec (2014); pp: 23-33
[13] Zhenyu Heb, Xinge Youb, Yuan Yan Tanga, “Writer identification
using global wavelet-based features”, Elsevier publication,
Neurocomputing (2008), page: 1832–1841.
[14] Zhenyu He, Yuan Yan Tang, Xinge You, “A Contourlet -based method
for writer identification”, IEEE International Conference on Systems,
Man and Cybernetics,Vol.1, 10-12 Oct 2005, Page: 364-368.
[15] Marius Bulacu, Lambert Schomaker, Louis Vuurpijl. (2003), “Writer
Identification Using Edge-Based Directional Features”, Proceedings of
the Seventh International Conference on Document Analysis and
Recognition.
[16] Gadkari, Dhanashree, “Image Quality Analysis Using GLCM”, UCF
Electronic Theses and Dissertations, 2004-12-01.
[17] Saranya K, Vijaya M.S, “An interactive Tool for Writer Identification
based on Offline Text Dependent Approach”, International Journal of
Advanced Research in Artificial Intelligence, Vol. 2, No. 1, 2013. [18] CRAMMER Koby, Yoram Singer, “On the Algorithmic Implementation
of Multi-class Kernel-based Vector Machines”, Journal of Machine
Learning Research, MIT Press, Cambridge, Vol.2 Page 265-292.
@article{"International Journal of Information, Control and Computer Sciences:70894", author = "T. Thendral and M. S. Vijaya and S. Karpagavalli", title = "Prediction of Writer Using Tamil Handwritten Document Image Based on Pooled Features", abstract = "Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.
", keywords = "Classification, Feature extraction, Support vector
machine, Training, Writer.", volume = "9", number = "6", pages = "1579-7", }
