Ensemble Neural Network in Classifying Handwritten Arabic Numerals

Kathirvalavakumar Thangairulappan, Palaniappan Rathinasamy

Department of Computer Science, V. H. N. S. N. College, Virudhunagar, India.

DOI: 10.4236/jilsa.2016.81001                

Abstract

A method has been proposed to classify handwritten Arabic numerals in its compressed form using partitioning approach, Leader algorithm and Neural network. Handwritten numerals are represented in a matrix form. Compressing the matrix representation by merging adjacent pair of rows using logical OR operation reduces its size in half. Considering each row as a partitioned portion, clusters are formed for same partition of same digit separately. Leaders of clusters of partitions are used to recognize the patterns by Divide and Conquer approach using proposed ensemble neural network. Experimental results show that the proposed method recognize the patterns accurately.

Keywords

Handwritten Numerals, Divide and Conquer, Cluster, Leader Algorithm, Neural Network, Ensemble, Classification

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Kodada Basappa, B. and Shivakumar, K.M. (2013) Unconstrained Handwritten Kannada Numeral Recognition. International Journal of Information and Electronics Engineering, 3, 230-232.
[2]	Mamta, G. and Ahuja, D. (2013) A Novel Approach to Recognize the Off-Line Handwritten Numerals Using MLP and SVM Classifiers. International Journal of Computer Science & Engineering Technology, 4, 953-958.
[3]	Meier, U., Ciresan, D.C., Gambardella, L.M. and Schmidhuber, J. (2011) Better Digit Recognition with a Committee of Simple Neural Nets. International Conference on Document Analysis and Recognition, 1250-1254. http://dx.doi.org/10.1109/icdar.2011.252
[4]	Monu, A., Gupta, N., Shreelekshmi, R. and Narasimha Murty, M. (2005) Efficient Pattern Synthesis for Nearest Neighbour Classifier. Pattern Recognition, 38, 2200-2203. http://dx.doi.org/10.1016/j.patcog.2005.03.029
[5]	Cleuziou, G. and Moreno, J.G. (2015) Kernel Methods for Point Symmetry Based Clustering. Pattern Recognition, 48, 2812-2830. http://dx.doi.org/10.1016/j.patcog.2015.03.013
[6]	Mitra, P., Murthy, C.A. and Pal, S.K. (2002) Unsupervised Feature Selection Using Feature Similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 301-312. http://dx.doi.org/10.1109/34.990133
[7]	Ravindra Babu, T., Narasimha Murty, M. and Agrawal, V.K. (2007) Classification of Run-Length Encoded Binary Data. Pattern Recognition, 40, 321-323. http://dx.doi.org/10.1016/j.patcog.2006.05.002
[8]	Akimov, A., Kolesnikov, A. and Franti, P. (2007) Lossless Compression of Map Contours by Context Tree Modelling of Chain Codes. Pattern Recognition, 40, 944-952. http://dx.doi.org/10.1016/j.patcog.2006.08.005
[9]	Vijaya Kumar, V., Srikrishna, A., Raveendra Babu, B. and Radhika Mani, M. (2010) Classification and Recognition of Handwritten Digits by Using Mathematical Morphology. Sadhana, 35, 419-426. http://dx.doi.org/10.1007/s12046-010-0031-z
[10]	Chen, B., Zhu, B.L. and Nakagawa, M. (2010) Effects of a Large Amount of Artificial Patterns for On-Line Handwritten Japanese Character Recognition. Proceedings of the 2nd China-Korea-Japan Joint Workshop on Pattern Recognition (CKJPR2010), Fukuoka, 90-93.
[11]	Park, H.-S. and Lee, S.-W. (1996) Off-Line Recognition of Large-Set Handwritten Characters with Multiple Hidden Markov Models. Pattern Recognition, 29, 231-244. http://dx.doi.org/10.1016/0031-3203(95)00081-X
[12]	Dhandra, B.V., Benne, R.G., Hangarge, M., Telugu, K. and Handwritten, D. (2011) Numeral Recognition with Probabilistic Neural Network: A Script Independent Approach. International Journal of Computer Applications, 26, 11-16.
[13]	Sarangi Pradeepta, K., Sahoo, A.K. and Ahmed, P. (2012) Recognition of Isolated Handwritten Oriya Numerals Using Hopfield Neural Network. International Journal of Computer Applications, 40, 36-42.
[14]	Noor Shatha, M., Mohammed Ihab, A. and George, L.E. (2011) Handwritten Arabic (Indian) Numerals Recognition Using Fourier Descriptor and Structure Base Classifier. Journal of Al-Nahrain University, 14, 215-224.
[15]	Kumar, P.D., Som, T., Yadav, S.K. and Singh, M.K. (2012) Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric. Journal of Signal and Information Processing, 3, 208-214. http://dx.doi.org/10.4236/jsip.2012.32028
[16]	Rajashekararadhya, S.V. and Vanaja Ranjan, P. (2009) Handwritten Numeral/Mixed Numerals Recognition of South-Indian Scripts: The Zone Based Feature Extraction Method. Journal of Theoretical and Applied Information Technology, 5, 63-79.
[17]	Asthana, S., Haneef, F. and Bhujade, R.K. (2011) Handwritten Multiscript Numeral Recognition Using Artificial Neural Networks. International Journal of Soft Computing and Engineering, 1, 1-5.
[18]	Fatlawi, A.A., Ling, S.H. and Lam, H.K. (2014) A Comparison of Neural Classifiers for Graffiti Recognition. Journal of Intelligence Learning Systems and Applications, 6, 94-112. http://dx.doi.org/10.4236/jilsa.2014.62008
[19]	Vijaya, P.A., Murty, M.N. and Subramanian, D.K. (2004) Leaders-Subleaders: An Efficient Hierarchical Clustering Algorithm for Large Data Sets. Pattern Recognition Letters, 25, 505-513. http://dx.doi.org/10.1016/j.patrec.2003.12.013
[20]	Viswanath, P., Murty, M.N. and Bhatnagar, S. (2004) Fusion of Multiple Approximate Nearest Neighbor Classifier for Fast and Efficient Classification. Information Fusion, 5, 239-250. http://dx.doi.org/10.1016/j.inffus.2004.02.003