Comparative Recognition of Handwritten Gurmukhi Numerals Using Different Feature Sets and Classifiers

This paper is published in proceedings of International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT2012) Proceedings published in International Journal of Computer Applications® (IJCA)

Title: Comparative Recognition of Handwritten Gurmukhi Numerals Using Different Feature Sets and Classifiers

Comparative Recognition of Handwritten Gurmukhi Numerals Using Different Feature Sets and Classifiers

Authors:
Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani

Abstract:
Recently there is an emerging trend in the research to recognize handwritten characters and numerals of many Indian languages and scripts. In this manuscript we have practiced the recognition of handwritten Gurmukhi numerals using three feature sets and three classifiers. Among three feature sets, first feature set is comprised of distance profiles having 128 features. Second feature set is comprised of different types of projection histograms having 190 features. Third feature set is comprised of zonal density and Background Directional Distribution (BDD) forming 144 features. The three classifiers used are SVM, PNN and K-NN. The SVM classifier is used with RBF (Radial Basis Function) kernel. We have observed the 5-fold cross validation accuracy in the case of each feature set and classifier. We have obtained the optimized result with each combination of feature set and classifier by adjusting the different parameters. The results are compared and trends of result in each combination of feature set and classifier with varying parameters is also discussed. With PNN and K-NN the highest results are obtained using third feature set as 98.33% and 98.51% respectively while with SVM the highest result is obtained using second feature set as 99.2%. The results with SVM for all feature sets are higher than the results with PNN and K-NN.

Keywords:
Handwritten Gurmukhi numeral recognition, Zonal density, Distance Profiles, Background Directional Distribution (BDD), SVM, PNN, K-NN, RBF kernel.

References:

1. G.S. Lehal, Nivedan Bhatt, 2000. A Recognition System for Devnagari and English Handwritten Numerals, Proc. ICMI, Springer, pp. 442-449.
2. Reena Bajaj, Lipika Dey, Shantanu Chaudhuri, February 2002. Devnagari numeral recognition by combining decision of multiple connectionist classifiers, Sadhana Vol. 27, Part 1, pp. 59–72.
3. U. Bhattacharya, B. B. Chaudhuri, 2003. A Majority Voting Scheme for Multiresolution Recognition of Handprinted Numerals, icdar, vol. 1, pp.16, Seventh International Conference on Document Analysis and Recognition (ICDAR’03) – Volume 1, pp. 29-33.
4. U. Pal, T. Wakabayashi, N. Sharma, F. Kimura, 2007. Handwritten Numeral Recognition of Six Popular Indian Scripts, Document Analysis and Recognition,. ICDAR, Vol.2, pp.749-753.
5. P.M. Patil, T.R. Sontakke, 2007. Rotation, scale and translation invariant handwritten Devanagari numeral character recognition using general fuzzy neural network, Pattern Recognition, Elsevier, Vol. 40, Issue 7, pp. 2110-2117, July.
6. Apurva A. Desai, 2010. Gujarati Handwritten Numeral Optical Character Reorganization through Neural Network, Pattern Recognition, Vol. 43, Issue 7, pp. 2582-89
7. Shailedra Kumar Shrivastava, Sanjay S. Gharde, October 2010. Support Vector Machine for Handwritten Devanagari Numeral Recognition, International Journal of Computer Applications, (July 2010), Vol. 7, No. 11, pp. 9-14
8. Mahesh Jangid, Kartar Singh, Renu Dhir, Rajneesh Rani, 2011, Performance Comparison of Devanagari Handwritten Numerals Recognition, Internation Journal of Computer Applications, (May 2011), Vol. 22, No.1,pp. 1-6.
9. Kartar Singh Siddharth, Mahesh Jangid, Renu Dhir, Rajneesh Rani, 2011. Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional Distribution Features, IJCSE, (June 2011), Vol. 3, No. 6, pp. 2332-2345.
10. Chih-Chung Chang and Chih-Jen Lin, 2001. LIBSVM: a library for support vector machines,. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

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Handwritten Gurmukhi Numeral Recognition using Different Feature Sets

This paper is published in IJCA (International Journal on Computer Applications) Volume 28 No. 2, August 2011 issue

Title: Handwritten Gurmukhi Numeral Recognition using Different Feature Sets

Authors:
Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani

Abstract:
Recently there is an emerging trend in the research to recognize handwritten characters and numerals of many Indian languages and scripts. In this manuscript we have practiced the recognition of handwritten Gurmukhi numerals. We have used three different feature sets. First feature set is comprised of distance profiles having 128 features. Second feature set is comprised of different types of projection histograms having 190 features. Third feature set is comprised of zonal density and Background Directional Distribution (BDD) forming 144 features. The SVM classifier with RBF (Radial Basis Function) kernel is used for classification. We have obtained the 5-fold cross validation accuracy as 99.2% using second feature set consisting of 190 projection histogram features. On third and first feature sets recognition rates 99.13% and 98% are observed. To obtain better results pre-processing of noise removal and normalization processes before feature extraction are recommended, which are also practiced in our approach.

Keywords:
Handwritten Gurmukhi numeral recognition, Zonal density, Projection histogram, Distance Profiles, Background Directional Distribution (BDD), SVM classifier, RBF kernel

Citation:
Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani, “Handwritten Gurmukhi Numeral Recognition using Different Feature Sets”, International Journal on Computer Applications (IJCA), Volume 28, No. 2, pp. 20-24, August 2011.

References:

1. G.S. Lehal, Nivedan Bhatt, “A Recognition System for Devnagari and English Handwritten Numerals”, Proc. ICMI, Springer, pp. 442-449, 2000.
2. Reena Bajaj, LipikaDey, ShantanuChaudhuri, “Devnagari numeral recognition by combining decision of multiple connectionist classifiers”, Sadhana Vol. 27, Part 1, pp. 59–72, February 2002.
3. U. Bhattacharya, B. B. Chaudhuri, “A Majority Voting Scheme for Multiresolution Recognition of Handprinted Numerals,” Seventh International Conference on Document Analysis and Recognition (ICDAR) – Volume 1, pp. 16, 2003.
4. U. Bhattacharya, S.K. Parui, B. Shaw, K. Bhattacharya, “Neural Combination of ANN and HMM for Handwritten Devanagari Numeral Recognition”, Tenth International Workshop on Frontiers in Handwriting Recognition, 2006.
5. U. Pal, T. Wakabayashi, N. Sharma, F. Kimura, “Handwritten Numeral Recognition of Six Popular Indian Scripts,” Proc. International Conference on Document Analysis and Recognition (ICDAR), Vol.2, pp.749-753, 2007.
6. P.M. Patil, T.R. Sontakke, “Rotation, scale and translation invariant handwritten Devanagari numeral character recognition using general fuzzy neural network”, Pattern Recognition, Elsevier, Vol. 40, Issue 7, pp. 2110-2117, July 2007.
7. R.J. Ramteke, S.C. Mehrotra, “Recognition of Handwritten Devanagari Numerals”, International Journal of Computer Processing of Oriental Languages, Chinese Language Computer Society & World Scientific Publishing Company, 2008.
8. G.G. Rajput, S.M. Mali, “Fourier Descriptor Based Isolated Marathi Handwritten Numeral Recognition”, International Journal of Computer Applications(IJCA), Vol. 3, No. 4, June 2010.
9. Apurva A. Desai, “Gujarati Handwritten Numeral Optical Character Reorganization through Neural Network”, Pattern Recognition, Vol. 43, Issue 7, pp. 2582-89, July 2010.
10. D. Sharma, G. S. Lehal, PreetyKathuria, “Digit Extraction and Recognition from Machine Printed Gurmukhi Documents”, MORC Spain, 2009.
11. Ubeeka Jain, D. Sharma, “Recognition of Isolated Handwritten Characters of Gurumukhi Script using Neocognitron”, International Journal of Computer Applications (IJCA),Vol. 4, No. 8, 2010.
12. Shailedra Kumar Shrivastava, Sanjay S. Gharde, “Support Vector Machine for Handwritten Devanagari Numeral Recognition”, International Journal of Computer Applications (IJCA), Vol. 7, No. 11, October 2010.
13. Mahesh Jangid, Kartar Singh, Renu Dhir, Rajneesh Rani, “Performance Comparison of Devanagari Handwritten Numerals Recognition”, Internation Journal of Computer Applications (IJCA), Vol. 22, No.1, May 2011.
14. Kartar Singh Siddharth, Mahesh Jangid, Renu Dhir, Rajneesh Rani, “Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional Distribution Features”, International Journal of Computer Science and Engineering (IJCSE), Vol. 3, No. 6, June 2011.
15. Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

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