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Recognizing Tamil Palm-Leaf Manuscript Characters Using Hybridized Human Perception Based Features


Affiliations
1 Department of Department of Electronics and Communication Engineering, Sona College of Technology, India
     

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In this paper, we present a zoning strategy for recognizing manuscript character images, based on human structural perception of characters. The deficiencies in a uniform zoning approach are filled by drawing significant information using the proposed method. The obtained feature set when applied on a SVM classifier, substantially improves the recognition rate for character images having structural variation at significant regions of characters. As a initiative, we have formulated the Tamil Palm-Leaf Character dataset. Preliminary results show that the incorporation of this hybridized zoning approach has improved the symbol recognition rate to 9.06% (from 81.07% to 90.13%). The average rejection rate has been nullified using this generic non-symmetrical zoning for the proposed dataset.

Keywords

Manuscript Character Recognition, Visual perception, Triangular Zoning, Shape Based Zoning, Significant Zone Slicing.
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  • D.B. Diskalkar, “Materials used for Indian Epigraphical Records”, Bhandarkar Oriental Research Institute, 1979.
  • G. John Samuel, “Uthirum Malargal”, The Institute of Asian Studies, 1994
  • G.John Samuel, “Kumari Muthal Warsaw Varai”, The Institute of Asian Studies, 1994.
  • UNESCO, “Memory of the World Programme”, Available at http://www.unesco.org/new/en/communication-and-information/flagship-project-activities/memory-of-the-world/homepage/, Accessed at 2020.
  • National Mission for Manuscript-The Republic of India, Available at http://www.namami.org/, Accessed at 2020.
  • Tamil Virtual Academy, Available at http://www.tamilvu.org/library/suvadi/html/index.htm, Accessed at 2020.
  • The Institute of Asian Studies, Available at www.instituteofasianstudies.com, Accessed at 2020.
  • Project Madurai, Available at http://www.projectmadurai.org/pmworks.html, Accessed at 2020.
  • Chinmaya International Foundation (CIF), Available at http://www.chinfo.org, Accessed at 2020.
  • Tara Prakashana, Available at http://www.taraprakashana.org/, Accessed at 2020.
  • M. Diem and R. Sablatnig, “Recognizing Characters of Ancient Manuscripts”, Proceedings of Computer Vision and Image Analysis of Art, pp.1-13, 2010.
  • N. Barbuti and T. Caldarola, “An Innovative Character Recognition for Ancient Book and Archival Materials: A Segmentation and Self-learning Based Approach”, Proceedings of International Conference on Digital Libraries and Archives, pp. 261-270, 2012.
  • K. Pratikakis, I. Petridis, S. Konidaris and S.J. Perantonis, “An Efficient Segmentation-Free Approach to Assist Old Greek Handwritten Manuscript OCR”, Pattern Analysis and Applications, Vol. 8, No. 4, pp. 305-320, 2006.
  • Arit Thammano and Sakkayaphop Pravesjit, “Recognition of Archaic Lanna Handwritten Manuscripts using a Hybrid Bio-Inspired Algorithm”, Memetic Computing, Vol. 7, No. 1, pp. 1-17, 2015.
  • Oivind Due Trier and Anil K. Jain and Torfinn Taxt, “Feature Extraction Methods for Character Recognition- A Survey”, Pattern Recognition, Vol. 29, No. 4, pp. 641-662, 1996.
  • S. Iamsa-At and P. Horata, “Handwritten Character Recognition using Histograms of Oriented Gradient Features in Deep Learning of Artificial Neural Network”, Proceedings of International Conference on IT Convergence and Security, pp. 1-5, 2013.
  • Hari Vasudevan, Abhijit R. Joshi, Narendra M. Shekokar and Parshuram M. Kamble, “Handwritten Marathi Character Recognition using R-HOG Feature”, Proceedings of International Conference on Advanced Computing Technologies and Applications, pp. 266-274, 2015.
  • K. Mehrotra, S. Jetley and S. Belhe, “Unconstrained Handwritten Devanagari Character Recognition using Convolutional Neural Networks”, Proceedings of International Conference on Multilingual OCR, pp. 1-5, 2015.
  • V.K. Govindan and A.P. Shivaprasad, “Character Recognition: A Review”, Pattern Recognition, Vol. 23, No. 7, pp. 671-683, 1990.
  • J.V. Moreau, Y. Lecourtier and C. Olivier, “A Structural Statistical Feature Based Vector for Handwritten Character Recognition”, Pattern Recognition Letters, Vol. 19, No. 7, pp. 629-641, 1998.
  • P.P. Roy, U. Pal, J. Llados and M. Delalandre, “Multi-Oriented and Multi-Sized Touching Character Segmentation using Dynamic Programming”, Proceedings of International Conference on Document Analysis and Recognition, pp. 11-15, 2009.
  • T.M. Rath and R. Manmatha, “Word Image Matching using Dynamic Time Warping”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 521-527, 2003.
  • R. Niels and L. Vuurpijl, “Dynamic Time Warping Applied to Tamil Character Recognition”, Proceedings of International Conference on Document Analysis and Recognition, pp. 730-734, 2005.
  • L. Prasanth, V. Jagadeesh Babu, R. Raghunath Sharma and G.V. Prabhakara Rao, “Elastic Matching of Online Handwritten Tamil and Telugu Scripts using Local Features”, Proceedings of International Conference on Document Analysis and Recognition, pp. 1028-1032, 2007.
  • K.P. Soman, R. Loganathan and V. Ajay, “Machine Learning with SVM and Other Kernel Methods”, PHI Learning Publisher, 2009.
  • N. Shanthi and K. Duraiswamy, “A Novel SVM-Based Handwritten Tamil Character Recognition System”, Pattern Analysis and Applications, Vol. 13, No. 2, pp. 173-180, 2010.
  • D. Impedovo and G. Pirlo, “Zoning Methods for Handwritten Character Recognition: A Survey”, Pattern Recognition, Vol. 47, No. 3, pp. 969-981, 2014.
  • Jun Cao, M. Ahmadi and M. Shridhar, “Recognition of Handwritten Numerals with Multiple Feature and Multistage
  • Classifier”, Pattern Recognition, Vol. 28, No. 2, pp. 153-160, 1995.
  • The Hindu, “World Classical Tamil Conference - A Perspective”, Available at http://www.thehindu.com/opinion/op-ed/world-classical-tamil-conference-a-perspective/article444941.ece, Accessed at 2012.
  • S. Sundaram and A.G. Ramakrishnan, “Performance Enhancement of Online Handwritten Tamil Symbol Recognition with Re-Evaluation Techniques”, Pattern Analysis and Applications, Vol. 17, No. 3, pp. 587-609, 2014.
  • N. K. Garg, L. Kaur and M. Jndal, “Recognition of Offline Handwritten Hindi Text using Middle Zone of the Words”, Proceedings of International Conference on Computer and Information Science, pp 325-328, 2015.
  • A.K. Bhunia, A. Das, P.P. Roy and U. Pal, “A Comparative Study of Features for Handwritten Bangla Text Recognition”, Proceedings of International Conference on Document Analysis and Recognition, pp. 636-640, 2015.
  • Partha Pratim Roy, Ayan Kumar Bhunia, Ayan Das, Prasenjit Dey and Umapada Pal, “HMM-based Indic handwritten Word Recognition using Zone Segmentation”, Pattern Recognition, Vol. 47, No. 1, pp. 1-24, 2016.
  • Digital Manuscript Gallery, Available at http://www.bdu.ac.in/suvadi/1.1/, Accessed at 2020.
  • Jean Luc Chevillard, “A Proposal for the Digital Encoding of Palm-Leaf Tamil Manuscripts”, Proceedings of International Conference on Tamil Internet, pp. 109-121, 2003.
  • T.R. Vijaya Lakshmi, Panyam Narahari Sastry and T.V. Rajinikanth, “A Novel 3D Approach to Recognize Telugu Palm Leaf Text”, International Journal Engineering Science and Technology, Vol. 12, No. 3, pp. 34-45, 2016.
  • Narahari Sastry Panyam and N.V. Koteswara Rao, “Modeling of Palm Leaf Character Recognition System using Transform based Techniques”, Pattern Recognition Letters, Vol. 84, No. 2, pp. 29-34, 2016.
  • Randolph Blake and Robert Sekuler, “Perception”, McGraw-Hill Higher Education, 2005.
  • K.C. Leung and C.H. Leung, “Recognition of Handwritten Chinese Characters by Critical Region Analysis”, Pattern Recognition, Vol. 43, No. 3, pp 949-961, 2010.
  • M. Blumenstein, B. Verma and H. Basli, “A Novel Feature Extraction Technique for Recognition of Segmented Handwritten Characters”, Proceedings of International Conference on Document Analysis and Recognition, pp. 122-134, 2003.
  • C.Y. Suen, J. Guo and Z.C. Li, “Analysis and Recognition of Alphanumeric Handprints by Parts”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 24, No. 4, pp. 614-631, 1994.
  • F. Bortolozzi and C.Y. Suen, “Segmentation and Recognition of Handwritten Dates: An HMM-MLP Hybrid Approach”, Document Analysis and Recognition, Vol. 6, No. 4, pp. 248-262, 2003.
  • L.S. Oliveira, R. Sabourin, F. Bortolozzi and C.Y. Suen, “Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 11, pp. 1438-1454, 2002.
  • Glenn Baptista and K.M. Kulkarni, “A High Accuracy Algorithm for Recognition of Handwritten Numerals”, Pattern Recognition, Vol. 21, No. 4, pp. 287-291, 1988.
  • A.L. Koerich and P.R. Kalva, “Unconstrained Handwritten Character Recognition using Metaclasses of Characters”, Proceedings of IEEE International Conference on Image Processing, pp. 542-545, 2005.
  • B. Verma, J. Lu, M. Ghosh and R. Ghosh, “A Feature Extraction Technique for Online Handwriting Recognition”, Proceedings of IEEE International Joint Conference on Neural Networks, pp. 1337-1341, 2004.
  • S. Singh and M. Hewitt, “Cursive Digit and Character Recognition in CEDAR Database”, Proceedings of IEEE International Conference on Image Processing, pp. 569-572, 2000.
  • G. Pirlo and D. Impedovo, “Adaptive Membership Functions for Handwritten Character Recognition by Voronoi-Based Image Zoning”, IEEE Transactions on Image Processing, Vol. 21, No. 9, pp. 3827-3837, 2012.
  • Sung Hyuk Cha, C.C. Tappert and S.N. Srihari, “Optimizing Binary Feature Vector Similarity Measure using Genetic Algorithm and Handwritten Character Recognition”, Proceedings of IEEE International Conference on Document Analysis and Recognition, pp. 662-665, 2003.
  • Atul Negi, Shanker, K. Nikhil and Chandra Kanth Chereddi, “Localization, Extraction and Recognition of Text in Telugu Document Images”, Proceedings of International Conference on Document Analysis and Recognition, pp. 1-12, 2003.
  • F. Kimura and M. Shridhar, “Handwritten Numerical Recognition based on Multiple Algorithms”, Pattern Recognition, Vol. 24, No. 10, pp 969-983, 1991.
  • Francesco Camastra and Alessandro Vinciarelli, “Combining Neural Gas and Learning Vector Quantization for Cursive Character Recognition”, Neurocomputing, Vol. 51, pp. 147-159, 2003.
  • C.Y. Suen, C. Nadal, R. Legault, T.A. Mai and L. Lam, “Computer Recognition of Unconstrained Handwritten Numerals”, Proceedings of the IEEE, Vol. 80, No. 7, pp. 1162-1180, 1992.
  • P. Vanaja Ranjan and V.N. Manjunath Aradhya, “Isolated Handwritten Kannada and Tamil Numeral Recognition: A Novel Approach”, Proceedings of International Conference on Emerging Trends in Engineering and Technology, pp. 1192-1195, 2008.
  • S.V. Rajashekararadhya and P.V. Ranjan, “Support Vector Machine based Handwritten Numeral Recognition of Kannada Script”, Proceedings of IEEE International Conference on Advance Computing, pp. 381-386, 2009.
  • S.V. Rajashekararadhya and P.V. Ranjan, “Zone Based Hybrid Feature Extraction Algorithm for Handwritten Numeral Recognition of South Indian Scripts”, Proceedings of IEEE International Conference on Contemporary Computing, pp. 138-148, 2009.
  • S.V. Rajashekararadhya and P.V. Ranjan, “The Zone-Based Projection Distance Feature Extraction Method for Handwritten Numeral/Mixed Numerals Recognition of Indian Scripts”, Proceedings of IEEE International Conference on Frontiers in Handwriting Recognition, pp. 617-622, 2010.
  • Chun Che Fung and R. Chamchong, “A Review of Evaluation of Optimal Binarization Technique for Character Segmentation in Historical Manuscripts”, Proceedings of 3rd International Conference on Knowledge Discovery and Data Mining, pp. 236-240, 2010.
  • Chun Cheand Wong and Kok Wai, “Comparing Binarisation Techniques for the Processing of Ancient Manuscripts”, Proceedings of International Conference on Cultural Computing, pp. 55-64, 2010.
  • R.S. Sabeenian, M.E. Paramasivam and P.M. Dinesh, “Appraisal of Localized Binarization Methods on Tamil Palm-leaf Manuscripts”, Proceedings of IEEE International Conference on Wireless Communications, Signal Processing and Networking, pp. 1-13, 2016.
  • N Otsu, “A Threshold Selection Method from Gray-Level Histograms”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979.
  • Dana Ballard and Chris Brown, “Computer Vision”, Prentice Hall, 1982.
  • M.Blumenstein and B. Verma, “A Novel Feature Extraction Technique for Recognition of Segmented Handwritten Characters”, Proceedings of International Conference on Document Analysis and Recognition, pp. 1-8, 2003.

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  • Recognizing Tamil Palm-Leaf Manuscript Characters Using Hybridized Human Perception Based Features

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Authors

Paramasivam Muthan Eswaran
Department of Department of Electronics and Communication Engineering, Sona College of Technology, India
Dinesh Manib
Department of Department of Electronics and Communication Engineering, Sona College of Technology, India
Sabeenian Royappan Savarimuthu
Department of Department of Electronics and Communication Engineering, Sona College of Technology, India

Abstract


In this paper, we present a zoning strategy for recognizing manuscript character images, based on human structural perception of characters. The deficiencies in a uniform zoning approach are filled by drawing significant information using the proposed method. The obtained feature set when applied on a SVM classifier, substantially improves the recognition rate for character images having structural variation at significant regions of characters. As a initiative, we have formulated the Tamil Palm-Leaf Character dataset. Preliminary results show that the incorporation of this hybridized zoning approach has improved the symbol recognition rate to 9.06% (from 81.07% to 90.13%). The average rejection rate has been nullified using this generic non-symmetrical zoning for the proposed dataset.

Keywords


Manuscript Character Recognition, Visual perception, Triangular Zoning, Shape Based Zoning, Significant Zone Slicing.

References