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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SVM Classifier for Recognition of Handwritten Devanagari Numeral

This paper is published in proceedings of International Conference on Image Information Processing (ICIIP 2011), an IEEE conference

Title: SVM Classifier for Recognition of Handwritten Devanagari Numeral

SVM Classifier for Recognition of Handwritten Devnagari Numeral

Authors:
Mahesh Jangid, Renu Dhir, Rajneesh Rani, Kartar Singh

Abstract:
In this manuscript we recognize the handwritten Devnagari numerals. In our implementation we have used density and background directional distribution features for the zones, in which we divided the numeral samples already. We used the normalized images of samples of varying sizes of 32*32, 40*40 and 48*48. We divided these normalized images into 4*4 (16), 5*5 (25) and 6*6 (36) zones respectively to compute the features for each zone. In all the cases each zone is of size 8*8 pixels. Each zone contains 9 features consisting of one density feature and 8 backgrounds directional distribution features. The zonal density feature is computed by dividing the number of foreground pixels in each zone by total number of pixels in the zone i.e. 64. The other 8 features are based on directional distribution values of background in eight directions. These directional values are computed for each foreground pixel by summing up the value corresponding to neighbouring background pixels given in the specific mask for each direction. For each direction these directional distribution features are summed up for all pixels in each zone. Thus numbers of features finally used for recognition are 144, 225 and 324 for samples of respective sizes in increasing order. For classification purpose we have used SVM classifier with RBF kernel. Our dataset of handwritten Devnagari numerals used is provided by Indian Statistical Institute (ISI), Kolkata. Training data size is 18783 and testing data size is 3763, totally 22546. The optimum 5-fold cross validation accuracies of training data obtained for varying sizes of samples in increasing order are 98.76%, 98.91% and 98.94% respectively. By observing the cross validation results it is conclusive that at the cost of increasing the features size there is only minute increases in the performance. So we recommend 144 sized feature vector to recognize testing samples. The testing accuracy by using 144 features for 32*32 normalized samples observed is 98.51% which is prominent and cost-efficient.

Keywords:
Devanagari Numerals, Zoning density, Background directional distribution, SVM classifier

Citation:
Mahesh Jangid, Renu Dhir, Rajneesh Rani, Kartar Singh, “SVM Classifier for Recognition of Handwritten Devanagari Numeral”, Proceedings of International Conference on Image Information Processing (ICIIP 2011), 2011.

References:

1. U.Pal and B B Choudhuri, “Indian script character recognition: A survey” Pattern Recognition ,Vol 37,pp 1887-1899,2004.
2. R M K Sinha, “ A journey from Indian scripts processing to Indian language processing “, IEEE Ann. Hist. Computer, vol 31, no 1, pp 831, 2009.
3. I.K, Sethi and B. Chatterjee, “Machine Recognition of constrained Hand printed Devanagari”, Pattern Recognition, Vol. 9,pp. 69-75, 1977.
4. G S Lehal, Nivedan Bhatt, “A Recognition System for Devnagri and English Handwritten Numerals”, Proc. Of ICMI, 2000.
5. Reena Bajaj, Lipika Day, Santanu Chaudhari, “Devanagari Numeral Recognition by Combining Decision of Multiple Connectionist Classifiers”, Sadhana, Vol.27, Part-I, 59-72, 2002.
6. R.J.Ramteke, S.C.Mehrotra, “Recognition Handwritten Devanagari Numerals”, International journal 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, R.K.Roy and F. Kimura,”Indian muilti-script full pin-code string recognition for postal automation” in Proc. 10 th conf. Document Analysis Recognition, 2009, pp 456-460
9. 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.
10. S. Knerr, L. Personnaz, and G. Dreyfus. Single-layer learning revisited: a stepwise procedure for building and training a neural network. In J. Fogelman, editor, Neu-rocomputing: Algorithms, Architectures and Applications. Springer-Verlag, 1990.
11. U. H.-G. Kressel. Pairwise classication and support vector machines. In B. Scholkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods { Support Vector Learning, pages 255{268, Cambridge, MA, 1998. MIT Press.
12. 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
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.
16. Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin, “A Practical Guide to Support Vector Classification”, [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
17. http://www.isical.ac.in/~ujjwal/download/database.html.
18. Gonzalez, Woods and Eddins,”a book on Digital Image Processing Using MATLAB”

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HANDWRITTEN GURMUKHI CHARACTER RECOGNITION : M.Tech. Thesis

HANDWRITTEN GURMUKHI CHARACTER RECOGNITION

A Thesis
Submitted in Partial Fulfillment of the
Requirements for the Award of the Degree of

MASTER OF TECHNOLOGY

in

COMPUTER SCIENCE & ENGINEERING

By
Kartar Singh Siddharth
(Roll No. 09203008)

Under the Supervision of

Dr Renu Dhir     Associate Professor

And

Mrs. Rajneesh Rani Assistant Professor

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

DR B R AMBEDKAR NATIONAL INSTITUTE OF TECHNOLOGY
JALANDHAR

July 2011

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Handwritten Gurmukhi Character Recognition: An M.Tech. Thesis Report

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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Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional Distribution Features

This paper is published in IJCSE (International Journal on Computer Science and Engineering) Volume 3 Issue 6

Title: Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional Distribution Features

Authors:
Kartar Singh Siddharth, Mahesh Jangid, Renu Dhir, Rajneesh Rani

Abstract:
In this manuscript handwritten Gurmukhi character recognition for isolated characters is proposed. We have used some statistical features like zonal density, projection histograms (horizontal, vertical and both diagonal), distance profiles (from left, right, top and bottom sides). In addition, we have used background directional distribution (BDD) features. Our database consists of 200 samples of each of basic 35 characters of Gurmukhi script collected from different writers. These samples are pre-processed and normalized to 32*32 sizes. SVM, K-NN and PNN classifiers are used for classification. The performance comparison of features used in different combination with different classifiers is presented and analyzed. The highest accuracy obtained is 95.04% as 5-fold cross validation of whole database using zonal density and background distribution features in combination with SVM classifier used with RBF kernel.

Keywords:
isolated handwritten Gurmukhi character recognition; statistical features; zoning; density; projection histogram; distance profile; background directional distribution; SVM; K-NN; PNN; RBF kernel

Citation:
Kartar Singh Siddharth, Mahesh Jangid, Renu Dhir, Rajneesh Rani, “Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional Distribution Features”, International Journal on Computer Science and Engineering (IJCSE), Volume 3, Issue 6, pp. 2332-2345, June 2011.

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Performance Comparison of Devanagari Handwritten Numerals Recognition

Performance Comparison of Devanagari Handwritten Numerals Recognition

Abstract:
In this paper an automatic recognition system for isolated Handwritten Devanagari Numerals is proposed and compared the recognition rate with different classifier. We presented a feature extraction technique based on recursive subdivision of the character image so that the resulting sub-images at each iteration have balanced numbers of foreground pixels as possible. Database, provided by Indian Statistical Institute, Kolkata, have 22547 grey scale images written by 1049 persons and obtained 98.98% highest accuracy with SVM classifier. Results are compared with KNN and Quadratic classifier.

Authors:
Mahesh Jangid, Kartar Singh, Renu Dhir, Rajneesh Rani

Keywords:
Devanagari Numeral, Indian Script, SVM (Support Vector Machine), KNN, Quadratic

Citation:
Mahesh Jangid, Kartar Singh, Renu Dhir, Rajneesh Rani, “Performance Comparison of Devanagari Handwritten Numerals Recognition”, International Journal of Computer Applications, Volume 22-No.1, pp. 1-6, May 2011.

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Performance Comparison of Devanagari Handwritten Numerals Recognition

Handwritten Gurmukhi Character Recognition Using Zoning Density and Background Directional Distribution Features

This is my first research paper published in May-June issue of IJCSIT (International Journal of Computer Science and Information Technologies). IJCSIT Vol 2 Issue 3

Handwritten Gurumukhi Character Recognition Using Zoning Density and Background Directional Distribution Features

Abstract:
This paper presents an approach to recognize isolated handwritten Gurumukhi characters. Extracted features used are 16 zonal densities and the 8 background directional distribution features for each of 16 zones. All sample images of Gurumukhi characters used are normalized to 32*32 pixel sizes. Zoning density is computed by dividing number of foreground pixels in each zone by number of total pixels in the zone. Background directional distribution values are calculated for each foreground pixel by directional distribution of its neighbouring background pixels. The value for each directional distribution is computed by summing up the values specified in a mask for corresponding neighbour background pixels. A specific mask is used in particular direction. These feature values for each direction are summed up for all pixels in each zone. Thus adding 16 zoning density features and 128 background directional distribution features (8 features in each of 16 zones) we used feature vector containing 144 features in our experiment. SVM classifier with RBF kernel is used for classification. We have taken 200 samples of each of 35 basic Gurumukhi characters in our experiment, which are collected from 20 different writers each contributing to write 10 samples of each of 35 characters. Thus we have used total 7000 character samples. By training the classifier with whole dataset we obtained 95.04% 5-fold cross validation accuracy. By using dataset taken by 16 writers (size 5600) to train the system and testing the 1400 samples by another 4 unknown writers to classifier, we obtained 89.14% accuracy. By splitting the handwritten samples of each writer to train and test the system in the ratio of 4:1, we received 99.93% test accuracy for known writers. Thus we got 94.53% average test accuracy for known and unknown writers and 95.04% 5-fold cross validation accuracy.

Authors:
Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani

Keywords:
Isolated Handwritten Gurumukhi Character Recognition, Zoning Density, Background Directional Distribution, SVM Classifier.

Citation:
Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani, “Handwritten Gurumukhi Charater Recognition Using Zoning Density and Background Directional Features”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2, Issue 3, pp. 1036-1041, May-June 2011.

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