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Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON)

by Parveen Kumar, Nitin Sharma, Arun Rana
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 53 - Number 11
Year of Publication: 2012
Authors: Parveen Kumar, Nitin Sharma, Arun Rana
10.5120/8466-2387

Parveen Kumar, Nitin Sharma, Arun Rana . Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON). International Journal of Computer Applications. 53, 11 ( September 2012), 25-31. DOI=10.5120/8466-2387

@article{ 10.5120/8466-2387,
author = { Parveen Kumar, Nitin Sharma, Arun Rana },
title = { Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON) },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 53 },
number = { 11 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 25-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume53/number11/8466-2387/ },
doi = { 10.5120/8466-2387 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:54:32.024003+05:30
%A Parveen Kumar
%A Nitin Sharma
%A Arun Rana
%T Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON)
%J International Journal of Computer Applications
%@ 0975-8887
%V 53
%N 11
%P 25-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Neural Networks and SVM are recently being used in various kind of pattern recognition. As humans, it is easy to recognize numbers, letters, voices, and objects, to name a few. However, making a machine solve these types of problems is a very difficult task . Character Recognition has been an active area of research in the field of image processing and pattern recognition and due to its diverse applicable environment, it continues to be a challenging research topic. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognized handwritten character using the multi layer feed forward back propagation neural network without feature extraction and SVM classifier. Character data is used for training the neural network and SVM. The trained network is used for classification and recognition. For the neural network, each character is resized into 70x50 pixels, which is directly subjected to training. That is, each resized character has 3500 pixels and these pixels are taken as features for training the neural network. For the SVM classifier recognition model is divided in two phases namely, training and testing phase. In the training phase 25 features are extracted from each character and these features are used to train the SVM. In the testing phase SVM classifier is used to recognize the characters. The results show that by applying the proposed system, we reached a high accuracy for the problem of handwritten character recognition.

References
  1. Mori, S. Suen, C. Y and Kamamoto, K. 1992. "Historical review of OCR research and development," Proc. of IEEE, vol. 80, pp. 1029-1058.
  2. Koerich, A. L. Sabourin, R. Suen, C. Y. 2003 "Large off-line Handwritten Recognition: A survey", Pattern Analysis Application 6, 97-121.
  3. Arica, N and Yarman-Vural, F. 2001. "An Overview of Character Recognition Focused on Off-line Handwriting", IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 31 (2), pp. 216 - 233.
  4. Govindan, V. K and Shivaprasad, A. P. 1990. "Character Recognition – A review," Pattern Recognition, Vol. 23, no. 7, pp. 671- 683.
  5. Bellili, A. Gilloux, M. Gallinari. P, An MLPSVM combination architecture for offline handwritten digit recognition. International Journal on Document Analysis and Recognition, pp. 244-252. Springer Berlin / Heidelberg.
  6. Plamondon, R and Srihari, S. N. 2000 "On-line and off- line handwritten character recognition: A comprehensive survey,"IEEE. Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, no. 1, pp. 63-84.
  7. Bhattacharya, U and Chaudhuri, B. B. 2009. "Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals," IEEE Transaction on Pattern analysis and machine intelligence, Vol. 31, No. 3, pp. 444-457.
  8. Pal, U. Wakabayashi, T and Kimura, F. 2007. "Handwritten numeral recognition of six popular scripts," Ninth International conference on Document Analysis and Recognition ICDAR 07, Vol. 2, pp. 749-753.
  9. Mantas, J. 1986. "An overview of character recognition methodologies," Pattern Recognition, Vol. 19, Issue 6, pp. 425-430.
  10. Rafael C. Gonzalez, Richard E. woods and Steven L. Eddins. 2004. Digital Image Processing using MATLAB, Education, Dorling Kindersley, South Asia.
  11. Casey, R. G and Lecolinet, E. 1996. "A Survey of Methods and Strategies in Character Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 7, pp. 690-706.
  12. Rajavelu, A. Musavi, M. T and Shirvaikar, M. V. 1989. "A Neural Network Approach to Character Recognition," Neural Networks, 2, pp. 387-393.
  13. Hanmandlu, M. Mohan, K. R. M and H. Kumar. 1999. "Neural-based handwritten character recognition," in Proceedings of Fifth IEEE International Conference on Document Analysis and Recognition, ICDAR'99, pp. 241-244
  14. Vapnik. V. 1995. The Nature of Statistical Learning Theory. Springer, N. Y. ISBN 0-387-94559-8.
  15. Nasien, D. Haron, H and Sophiayati Yuhaniz, S. 2010. "Support Vector Machine (SVM) for English Handwritten Character Recognition," Computer Engineering and Applications (ICCEA), 2010 Second International Conference on, vol. 1, no. , pp. 249-252.
  16. K. P. Soman, Loganathan. R, V. Ajay "Machine Learning with SVM and other Kernel Methods", Prentice Hall of India.
  17. Mangal, M and Pratap Singh, M. 2006 "Handwritten English vowels recognition using hybrid evolutionary feed-forward neural network, Malaysian Journal of Computer Science, Vol. 19(2), pp. No. 169-187.
  18. Subhangi, D. C and Hiremath, P. S. 2009. "Handwritten English Character and Digit Recognition Using Multiclass SVM Classifiers and Using Structural Micro Features, International Journal of Recent Trends in Engineering, Vol. 2, No. 2, pp. 193-195.
  19. Ganapathy, V and leong liew, K. 2008. "Handwritten Character Recognition Using Multi-scale Neural Network Training Techniques, World Academy of Science, Engineering and Technology, Pp. 32-37.
  20. Pradeep, J. Srinivasan, E. Himavathi, S. 2011. "Neural Network based handwritten character recognition system without feature extraction" International conference on Computer, Communication and Electrical Technology, ICCCET, pp-40-44
  21. Arora, S. Bhattacharjee, D. Nasipuri, M. Malik, L. Kundu, M and Basu, D. K. 2010. "Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition" IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, pp-18-26.
Index Terms

Computer Science
Information Sciences

Keywords

Handwritten character recognition Pre-processing Segmentation Morphological operations Feed forward back propagation Neural Network Support vector machine

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