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Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM

by Ashutosh Aggarwal, Rajneesh Rani, Renu Dhir
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 8
Year of Publication: 2012
Authors: Ashutosh Aggarwal, Rajneesh Rani, Renu Dhir
10.5120/7371-0151

Ashutosh Aggarwal, Rajneesh Rani, Renu Dhir . Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM. International Journal of Computer Applications. 48, 8 ( June 2012), 39-44. DOI=10.5120/7371-0151

@article{ 10.5120/7371-0151,
author = { Ashutosh Aggarwal, Rajneesh Rani, Renu Dhir },
title = { Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 8 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number8/7371-0151/ },
doi = { 10.5120/7371-0151 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:43:35.431191+05:30
%A Ashutosh Aggarwal
%A Rajneesh Rani
%A Renu Dhir
%T Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 8
%P 39-44
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recognition of Indian languages is a challenging problem. In Optical Character Recognition (OCR), acharacter or symbol to be recognized can be machine printed or handwritten characters/numerals. Several approaches in the past have been proposed that deal with problem of recognition of numerals/character depending on the type of feature extracted and way of extracting them. In this paper also a recognition system for isolated Handwritten Devanagari Numerals has been proposed. The proposed system is based on the division of sample image into sub-blocks and then in each sub-block Strength of Gradient is accumulated in 8 standard directions in which Gradient Direction is decomposed resulting in a feature vector with dimensionality of 200. Support Vector Machine (SVM) is used for classification. Accuracy of 99. 60% has been obtained by using standard dataset provided by ISI (Indian Statistical Institute) Kolkata.

References
  1. Ivind due trier, Anil Jain, torfiinnTaxt, "A feature extraction method for character recognition-A survey ", Pattern Recg, vol 29, No 4, pp-641-662, 1996.
  2. SandhyaArora, DebotoshBhattacharjee, Mita Na-sipuri, D. K. Basu, M. Kundu, " Recognition of Non-Compound Handwritten DevnagariCharac-ters using a Combination of MLP and Minimum Edit Distance", International Journal of Computer Science and Security (IJCSS), Volume (4): Issue-1 pp 107-120.
  3. P M Patil, T R Sontakke," Rotation, scale and trans-lation invariant handwritten Devanagari numeral character recognition using general fuzzy neural network", Pattern Recognition, Elsevier, 2007.
  4. G S Lehal, Nivedan Bhatt, "A Recognition System for Devnagri and English Handwritten Numerals", Proc. Of ICMI, 2000.
  5. Reena Bajaj, Lipika Day, SantanuChaudhari, "Devanagari Numeral Recognition by Combining De-cision of Multiple Connectionist Classifiers", Sadhana, Vol. 27, Part-I, 59-72, 2002.
  6. R. J. Ramteke, S. C. Mehrotra, "Recognition Hand-written Devanagari Numerals", International jour-nal of Computer processing of Oriental languages, 2008.
  7. U. Bhattacharya, S. K. Parui, B. Shaw, K. Bhattacharya, "Neural Combination of ANN and HMM for Handwritten Devnagari Numeral Recognition".
  8. U. Pal, T. Wakabayashi, N. Sharma and F. Kimura, "Handwritten Numeral Recognition of Six Popular Indian Scripts", Proc. 9th ICDAR, Curitiba, Brazil, Vol. 2 (2007), 749-753.
  9. U. Bhattacharya and B. B. Chaudhuri, "Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals,"IEEE Trans. Pattern Anal. Mach. Intell. , vol. 31, no. 3, pp. 444–457, Mar. 2009.
  10. M. Hanmandlu and O. V. R. Murthy, "Fuzzy model based recognition of handwritten numerals," Pattern Recognit. , vol. 40, pp. 1840–1854, 2007.
  11. M. Hanmandlu, A. V. Nath, A. C. Mishra, and V. K. Madasu, "Fuzzy model based recognition of handwritten hindi numerals using bacterial foraging," in Proc. Int. Conf. Comput. Inf. Sci. , 2007, pp. 309–314.
  12. G. G. Rajput and S. M. Mali, "Fourier descriptor based isolated Marathi handwritten numeral recognition," Int. J. Comput. Appl. , vol. 3, no. 4,pp. 9–13, 2010.
  13. A. Elnagar and S. Harous, "Recognition of handwritten Hindi numerals using structural descriptors," J. Exp. Theor. Artif. Intell. , vol. 15, no. 3, pp. 299–214, 2003.
  14. U. Garain, M. P. Chakraborty, and D. Dasgupta, "Recognition of hand-written Indic script digits using clonal selection algorithm," in Lecture Notes in Computer Science 4163 , H. Bersini and J. Carneiro, Eds. New York: Springer-Verlag, 2006, pp. 256–266.
  15. U. Pal, R. K. Roy, K. Roy, and F. Kimura, "Indian multi-script full pin-code string recognition for postal automation," in Proc. 10th Conf. Document Anal. Recognit. , 2009, pp. 456–460.
  16. C. V. Lakshmi, R. Jain, and C. Patvardhan, "Handwritten Devnagari numerals recognition with higher accuracy," in Proc. Int. Conf. Comput. Intell. Multimedia Appl. , 2007, pp. 255–259.
  17. S. Basu, N. Das, R. Sarkar, M. Kundu, M. Nasipuri, and D. K. Basu,"A novel framework for automatic sorting of postal documents with multi-script address blocks," Pattern Recognit. , vol. 43, pp. 3507–3521, 2010.
  18. N. Sharma, U. Pal, F. Kimura, and S. Pal, "Recognition of offline hand-written Devnagari characters using quadratic classifier," in Proc. Indian Conf. Comput. Vis. Graph. Image Process. , 2006, pp. 805–816.
  19. Mahesh Jangid, RenuDhir and Rajneesh Rani,"A Novel Approach: Recognition of Devanagari Handwritten Numerals ", International Journal Of Electrical, Electronics And Computer Systems (Ijeecs) Volume 1, Issue 2, April 2011. ISSN: 2221-7258(Print) ISSN: 2221-7266.
  20. R. Jayadevan et. al "Offline Recognition of Devanagari Script: A Survey",IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications And Reviews, Vol. 41, No. 6, November 2011.
  21. U. Bhattacharya and B. B. Chaudhuri, "Databases for Research on Recognition of Handwritten Char-acters of Indian Scripts," Proc. Eighth Int?l Conf. Document Analysis and Recognition (ICDAR ?05), vol. 2, pp. 789-793, 2005.
  22. Cheng-Lin Liu, Kazuki Nakashima, Hiroshi Sako, Hiromichi Fujisawa, "Handwritten digit recognition: investigation of normalization and feature extraction techniques" Elseveir Pattern Recognition, 37 (2004) 265 – 279.
  23. Ashutosh Aggarwal et al. "Handwritten Devanagari Characters Recognition using Gradient Features" IJARCSSE, page 85-90, Vol. 2 Issue 5 May 2012.
  24. Akinoria Kawamura et. al. "Online Recognition of Freely handwritten Japaneese Characters Using Directional feature Densities" IEEE XPLore Digital Library 1992.
  25. Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001. Software available: http://www. csie. ntu. edu. tw/~cjlin/libsvm.
  26. Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin, "A Practical Guide to Support Vector Classification",[Online]. http://www. csie. ntu. edu. tw/~cjlin/papers/guide/guide. pdf
  27. Chih-Chung Chang, and Chih-Jen Lin, "LIBSVM: A Library for Support Vector Machines", [Online]. http://www. csie. ntu. edu. tw/~cjlin/papers/libsvm. pdf.
Index Terms

Computer Science
Information Sciences

Keywords

Devanagari Numeralrecognition Handwrittenrecognition Gradient Gradient Feature Extraction svm

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