Similar Shaped Ancient Tamil Character Recognition using Gradient Feature and Template Matching
Abstract
Recognition of similar shaped ancient Tamil character
is a difficult problem and in character recognition system most of the errors occur from similar shaped characters. Researchers have used many methods of feature extraction for ancient Tamil character recognition. In this paper we proposed a novel feature extraction technique to improve the recognition results of two similar shaped ancient Tamil characters. In this work, we have taken the samples of offline ancient Tamil characters from six different centuries. Thetechnique is based on F-ratio (Fisher Ratio), a statistical measure defined by the ratio between-class variance and within-class variance.F-ratio modifies the feature vector of two similar shape characters by weighting the feature elements. This weighting scheme enhances the feature elements that belongs to the distinguishable portions of the similar shaped characters and reduces the feature elements of the common portion of the characters, so that similar shaped characterscan be identified easily. We considered pair of similar shape ancient characters of different stone inscriptions and we noted that f-ratio based feature weighting shows better recognition results. The proposed system achieves a maximum recognition accuracy of 94% using gradient features and template matching method.
Keywords
Full Text:
PDFReferences
Bansal, V. and R.M.K. Sinha, “On how to describe shapes of Devanagari
characters and use them for recognition”, Proceedings of the Fifth
International Conference on Document Analysis and Recognition,
pp.410-413, 20-22 Sep 1999, DOI:10.1109/ICDAR.1999.791811
Le Song and Yuchi Lin, “Study on the Vision Reading Algorithm based
on Template Matching and Neural Network”, Proceedings of
International Joint Conference on Neural Networks, Orlando, Florida,
USA, August 12-17, 2007.
P.Subashini,M.Krishnaveni and N.Sridevi, “Period Prediction System for
Tamil Epigraphical Scripts based on support vector machine”,
Proceeding of International Conference on ICISIL 2011, Volume
,Patiala,India, pp. 23-30, March 9-11,2011 .
Deng, D., K.P. Chan and Y. Yu, “Handwritten Chinese character
recognition using spatial Gabor filters and self organizing feature maps”,
Proceeding of the IEEE International Conference on Image Processing,
Volume 3, pp. 940 - 944, 13-16 Nov 1994, DOI:
1109/ICIP.1994.413707.
Arica, N. and F.T. Yarman-Vural, “An overview of character recognition
focused on off-line handwriting”, IEEE Transactions on Systems, Man,
and Cybernetics, Part C: Applications and Reviews, vol.31, Issue.2, pp.
- 233, May 2001, DOI:10.1109/5326.941845.
Pal, U. and B.B. Chaudhuri, “Indian script character recognition: A
survey”, Pattern Recognition, vol. 37, no.9, pp. 1887-1899, 2004,
DOI:10.1016/j.patcog.2004.02.003.
Mantas, J., “An overview of character recognition methodologies”,
Pattern Recognition, Volume 19, Issue 6, pp. 425-430, 1986, DOI:
1016/0031-3203(86)90040-3.
Govindan, V.K. and A.P. Shivaprasad, “Character recognition - a
review”, Pattern Recognition, Volume 23 , Issue 7, pp.671 - 683, July
, DOI: 10.1016/0031- 3203(90)90091-X.
Chaudhuri, B.B. and U. Pal, “A complete printed bangla OCR system
Pattern Recognition”, Volume 31, Issue 5, 1 March 1998, Pages 531-549,
DOI:10.1016/S0031- 3203(97)00078-2.
N.Dhamayanthi, and P.Thangavel, "Handwritten Tamil character
recognition using neural network", in: Proc. Of Tamil Internet 2000, K.
Kalyanasundram and S. Senthil Nathan (Eds.), Singapore, pp. 171-176,
July 22-24, 2000.
R.M. Bozinovic, S.N. Srihari, "Off-Line Cursive Script Word
Recognition," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 11, no. 1, pp. 68-83, Jan., 1989, DOI:
1109/34.23114.
R. Jagadeesh Kannan and R. Prabhakar, “An Improved Handwritten
Tamil Character Recognition System using Octal Graph”, Journal of
Computer Science 4 (7): 509-516, 2008 ISSN 1549-3636.
Chinnuswamy, P. and S.G. Khrishnamoorthy, “Recognition of hand
printed Tamil characters”, Pattern Recognition, Volume 12, Issue 3,
, pp. 141-152, DOI: 10.1016/0031- 3203(80)90038-2.
Suresh, R.M. and S. Arumugam, “Fuzzy contextfree grammar to
handwritten numerical recognition”, Proceedings. Third International
Conference on Computational Intelligence and Multimedia Applications,
pp. 459 - 463, 1999, DOI:10.1109/ICCIMA.1999.798574.Xiaoqing
Ding, Tao Wu. ”Character Independent Font Recognition on a Single
Chinese Character”. IEEE Transactions on pattern analysis and machine
intelligence, Vol.29, N0.2, February 2007.
Jianying Hu, Michael K. Brown, William Turin, "HMM Based On-Line
Handwriting Recognition," IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 18, no. 10, pp. 1039-1045, October 1996, DOI:
1109/34.541414.
Chang, C.H., “Simulated annealing clustering of Chinese words for
contextual text recognition”, Pattern Recognition, Volume 17, Issue 1,
pp. 57-66, 10 January 1996, DOI:10.1016/0167-8655(95)00080-1.
Tappert, C., C. Suen and T. Wakahara, “The state of the art in online
handwriting recognition”, IEEE Transactions on Pattern Analysis and
Machine Intelligence, Volume 12, Issue 8, Aug 1990, pp. 787 - 808, DOI:
1109/34.57669.
Yamada, H., K. Yamamoto and T. Saito, “A nonlinear normalization
method for hand printed Kanji character recognition-line density
equalization”, 9th International Conference on Pattern Recognition,
vol.1, pp. 172-175,14- 17 Nov 1988, DOI:10.1109/ICPR.1988.28198.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.