Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric

Affiliation(s)

Department of Applied Mathematics, Institute of Technology, Banaras Hindu U.

Department of Applied Mathematics, Institute of Technology, Banaras Hindu University, Varanasi, India.

Department of Statistics, Faculty of Science, Banaras Hindu University, Varanasi, India.

DST Centre for Interdisciplinary Mathematical Sciences, Faculty of Science, Banaras Hindu University, Varanasi, India.

Department of Applied Mathematics, Institute of Technology, Banaras Hindu U.

Department of Applied Mathematics, Institute of Technology, Banaras Hindu University, Varanasi, India.

Department of Statistics, Faculty of Science, Banaras Hindu University, Varanasi, India.

DST Centre for Interdisciplinary Mathematical Sciences, Faculty of Science, Banaras Hindu University, Varanasi, India.

ABSTRACT

In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The technique has been tested and found to be more accurate and faster. Characters is classified into 26 pattern classes based on appropriate properties. Features of the handwritten character images are extracted by DWT used with appropriate level of multiresolution technique, and then each pattern class is characterized by a mean vector. Distances from input pattern vector to all the mean vectors are computed by EDM. Minimum distance determines the class membership of input pattern vector. The proposed method provides good recognition accuracy of 90% for handwritten characters even with fewer samples.

In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The technique has been tested and found to be more accurate and faster. Characters is classified into 26 pattern classes based on appropriate properties. Features of the handwritten character images are extracted by DWT used with appropriate level of multiresolution technique, and then each pattern class is characterized by a mean vector. Distances from input pattern vector to all the mean vectors are computed by EDM. Minimum distance determines the class membership of input pattern vector. The proposed method provides good recognition accuracy of 90% for handwritten characters even with fewer samples.

Cite this paper

D. Kumar Patel, T. Som, S. Kumar Yadav and M. Kumar Singh, "Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric,"*Journal of Signal and Information Processing*, Vol. 3 No. 2, 2012, pp. 208-214. doi: 10.4236/jsip.2012.32028.

D. Kumar Patel, T. Som, S. Kumar Yadav and M. Kumar Singh, "Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric,"

References

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[5] Y. Mizukami, “A Handwritten Chinese Character Recognition System Using Hierarchical Displacement Extraction Based on Directional Features,” Pattern Recognition Letters, Vol. 19, No. 7, 1998, pp. 595-604. doi:10.1016/S0167-8655(98)00034-8

[6] A.-B. Wang and K.-C. Fan, “Optical Recognition of Handwritten Chineses Character by Hierarchical Radical Matching Method,” Pattern Recognition, Vol. 34, No. 1, 2001, pp. 15-35. doi:10.1016/S0031-3203(99)00207-1

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[8] A. Cheung, M. Bennamoun and N. W. Bergmann, “An Arabic OCR System Using Recognition-Based Segmentation,” Pattern Recognition, Vol. 34, No. 2, 2001, pp. 215-233. doi:10.1016/S0031-3203(99)00227-7

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[11] S. N. S. Rajasekharan and B. L. Deekshatulu, “Generation and Recognition of Printed Telugu Characters,” Computer Graphics and Image Processing, Vol. 6, No. 4, 1977, pp. 335-360. doi:10.1016/0146-664X(77)90028-4

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[14] A. A. Desai, “Gujarati Handwritten Numeral Optical Character Reorganization through Neural Network,” Pattern Recognition, Vol. 43, No. 7, 2010, pp. 2582-2589. doi:10.1016/j.patcog.2010.01.008

[15] N. Arica and F. T. Yarman-Vural, “An Overview of Character Recognition Focused on Off-Line Handwriting,” IEEE Transactions on Systems, Man, and Cybernetics— Part C: Applications and Reviews, Vol. 31, No. 2, 2001, pp. 216-233. doi:10.1109/5326.941845

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[21] R. Plamondon and S. N. Srihari, “On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2000, pp. 63-84. doi:10.1109/34.824821

[22] U. Pal and B. B. Chaudhari, “Indian Script Character Recognition: a Survey,” Pattern Recognition, Vol. 37, No. 9, 2004, pp. 1887-1899. doi:10.1016/j.patcog.2004.02.003

[23] J. Mantas, “An Overview of Character Recognition Methodologies,” Pattern Recognition, Vol. 19, No. 6, 1986, pp. 425-430. doi:10.1016/0031-3203(86)90040-3

[24] E. A. Fadhel and P. Bhattacharyya, “Application of a Steerable Wavelet Transform Using Neural Network for Signature Verification,” Pattern Analysis & Applications, Vol. 2, 1999, pp. 184-195. doi:10.1007/s100440050027

[25] R. E. Leary, “Unrestricted Off-Line Handwriting Recognition: A Preprocessing Approach,” Unpublished Paper Written at Rensselaer Polytechnic Institute, 2009, pp. 1-11.

[26] A. Mowlaei, K. Faez and A. T. Haghighat, “Feature Extraction with Wavelet Transform for Recognition of Isolated Handwritten Farsi or Arabic Characters and Numerals,” IEEE Digital Signal Processing, Vol. 2, No. 2, 2002, pp. 923-926.

[27] P. Wunsch and A. F. Laine, “Wavelet Descriptors for Multiresolution Recognition of Handprinted Characters,” Pattern Recognition, Vol. 28, No. 8, 1995, pp. 1237-1249. doi:10.1016/0031-3203(95)00001-G

[28] D. J. Romero, L. M. Seijas and A. M. Ruedin, “Directional Continuous Wavelet Transform Applied to Handwritten Numerals Recognition Using Neural Networks,” Java Caching System, Vol. 7, No. 1, 2007, pp. 66-71.

[29] S.-W. Lee, C.-H. Kim, H. Ma and Y. Y. Tang, “Multiresolution Recognition of Unconstrained Handwritten Numerals with Wavelet Transform and Multilayer Cluster Neural Network,” Pattern Recognition, Vol. 29, No. 12, 1996, pp. 1953-1961. doi:10.1016/S0031-3203(96)00053-2

[30] J. C. Lee, T. J. Fong and Y. F. Chang, “Feature Extraction for Handwritten Chinese Character Recognition Using X-Y Graphs Decomposition and Haar Wavelet,” IEEE International Conference on Signal and Image Processing Applications, 18-19 November 2009, pp. 10-14.

[31] D. Sharma and D. Gupta, “Isolated Handwritten Digit Recognition Using Adaptive Unsupervised Incremental Learning Technique,” International Journal of Computer Applications, Vol. 7, No. 4, 2010, pp. 27-33.

[32] T. Hastie and P. Y. Simard, “Metrics and Models for Handwritten Character Recognition,” Statistical Science, Vol. 13, No. 1, 1998, pp. 54-65. doi:10.1214/ss/1028905973

[33] S. Basu, N. Das, R. Sarkar, M. Kundu, M. Nasipuri and D. K. Basu, “A Hierarchical Approach to Recognition of Handwritten Bangla Characters,” Pattern Recognition, Vol. 42, No. 7, 2009, pp. 1467-1484. doi:10.1016/j.patcog.2009.01.008

[34] C.-L. Liu and C. Y. Suen, “A New Benchmark on the Recognition of Handwritten Bangla and Farsi Numeral Characters,” Pattern Recognition, Vol. 42, No. 12, 2009, pp. 3287-3295. doi:10.1016/j.patcog.2008.10.007

[35] M. E. Stevens, “Introduction to the Special Issue on Optical Character Recognition (OCR),” Pattern Recognition, Vol. 2, No. 3, 1970, pp. 147-150. doi:10.1016/0031-3203(70)90026-9

[36] J. C. Rabinow, “Whither OCR and Whence?” Datamation, 1969, pp. 38-42.

[1] G. Y. Chen, T. D. Bui and A. Krzyzak, “Contour-Based Handwritten Numeral Recognition Using Multiwavelets and Neural Networks,” Pattern Recognition, Vol. 36, No. 7, 2003, pp. 1597-1604. doi:10.1016/S0031-3203(02)00252-2

[2] J. Sung, S.-Y. Bang and S. J. Choi, “A Bayesian Network Classifier and Hierarchical Gabor Features for Handwritten Numeral Recognition,” Pattern Recognition Letters, Vol. 27, No. 1, 2006, pp. 66-75. doi:10.1016/j.patrec.2005.07.003

[3] C.-L. Liu and H. Sako, “Class-Specific Feature Polynomial Classifier for Pattern Classification and Its Application to Handwritten Numeral Recognition,” Pattern Recognition, Vol. 39, No. 4, 2006, pp. 669-681. doi:10.1016/j.patcog.2005.04.021

[4] A. Broumandnia and J. Shanbehzadeh, “Fast Zernike Wavelet Moments for Farsi Character Recognition,” Image and Vision Computing, Vol. 25, No. 5, 2007, pp. 717-726. doi:10.1016/j.imavis.2006.05.014

[5] Y. Mizukami, “A Handwritten Chinese Character Recognition System Using Hierarchical Displacement Extraction Based on Directional Features,” Pattern Recognition Letters, Vol. 19, No. 7, 1998, pp. 595-604. doi:10.1016/S0167-8655(98)00034-8

[6] A.-B. Wang and K.-C. Fan, “Optical Recognition of Handwritten Chineses Character by Hierarchical Radical Matching Method,” Pattern Recognition, Vol. 34, No. 1, 2001, pp. 15-35. doi:10.1016/S0031-3203(99)00207-1

[7] H.-Y. Kim and J. H. Kim, “Hierarchical Random Graph Represetation of Handwritten and Its Application to Hangul Recognition,” Pattern Recognition, Vol. 34, No. 2, 2001, pp. 108-201. doi:10.1016/S0031-3203(99)00222-8

[8] A. Cheung, M. Bennamoun and N. W. Bergmann, “An Arabic OCR System Using Recognition-Based Segmentation,” Pattern Recognition, Vol. 34, No. 2, 2001, pp. 215-233. doi:10.1016/S0031-3203(99)00227-7

[9] R. M. K. Sinha and H. N. Mahabala, “Machine Recognition of Devanagari Script,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 8, 1979, pp. 435-441.

[10] B. B. Chaudhuri and U. Pal, “A Complete Printed Bangla OCR System,” Pattern Recognition, Vol. 31, No. 5, 1998, pp. 531-549. doi:10.1016/S0031-3203(97)00078-2

[11] S. N. S. Rajasekharan and B. L. Deekshatulu, “Generation and Recognition of Printed Telugu Characters,” Computer Graphics and Image Processing, Vol. 6, No. 4, 1977, pp. 335-360. doi:10.1016/0146-664X(77)90028-4

[12] B. B. Chaudhuri, O. A. Kumar and K. V. Ramana, “Automatic Generation and Recognition of Telugu Script Characters,” IETE Journal of Rearch, Vol. 37, 1991, pp. 499-511.

[13] M. B. Sukhaswami, P. Seetharamulu and A. K. Pujari, “Recognition of Telugu Characters Using Neural Networks,” International Journal of Neural Systems, Vol. 6, No. 3, 1995, pp. 317-357. doi:10.1142/S0129065795000238

[14] A. A. Desai, “Gujarati Handwritten Numeral Optical Character Reorganization through Neural Network,” Pattern Recognition, Vol. 43, No. 7, 2010, pp. 2582-2589. doi:10.1016/j.patcog.2010.01.008

[15] N. Arica and F. T. Yarman-Vural, “An Overview of Character Recognition Focused on Off-Line Handwriting,” IEEE Transactions on Systems, Man, and Cybernetics— Part C: Applications and Reviews, Vol. 31, No. 2, 2001, pp. 216-233. doi:10.1109/5326.941845

[16] R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” Pearson Education, Upper Saddle River, 2002.

[17] S. Mallat, “A Wavelet Tour of Signal Processing,” Academic Press, Burlington, 2008.

[18] S. Theodoridis and K. Koutroumbas, “An Introduction to Pattern Recognition: A MATLAB Approach,” Elsevier, Burlington, 2010.

[19] R. O. Duda, P. E. Hart and D. G. Stork, “Pattern Classification,” Wily-India, 1993.

[20] R. C. Gonzalez and R. E. Woods, “Digital Image Processing Using MATLAB,” Pearson Education, Upper Saddle River, 2004.

[21] R. Plamondon and S. N. Srihari, “On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2000, pp. 63-84. doi:10.1109/34.824821

[22] U. Pal and B. B. Chaudhari, “Indian Script Character Recognition: a Survey,” Pattern Recognition, Vol. 37, No. 9, 2004, pp. 1887-1899. doi:10.1016/j.patcog.2004.02.003

[23] J. Mantas, “An Overview of Character Recognition Methodologies,” Pattern Recognition, Vol. 19, No. 6, 1986, pp. 425-430. doi:10.1016/0031-3203(86)90040-3

[24] E. A. Fadhel and P. Bhattacharyya, “Application of a Steerable Wavelet Transform Using Neural Network for Signature Verification,” Pattern Analysis & Applications, Vol. 2, 1999, pp. 184-195. doi:10.1007/s100440050027

[25] R. E. Leary, “Unrestricted Off-Line Handwriting Recognition: A Preprocessing Approach,” Unpublished Paper Written at Rensselaer Polytechnic Institute, 2009, pp. 1-11.

[26] A. Mowlaei, K. Faez and A. T. Haghighat, “Feature Extraction with Wavelet Transform for Recognition of Isolated Handwritten Farsi or Arabic Characters and Numerals,” IEEE Digital Signal Processing, Vol. 2, No. 2, 2002, pp. 923-926.

[27] P. Wunsch and A. F. Laine, “Wavelet Descriptors for Multiresolution Recognition of Handprinted Characters,” Pattern Recognition, Vol. 28, No. 8, 1995, pp. 1237-1249. doi:10.1016/0031-3203(95)00001-G

[28] D. J. Romero, L. M. Seijas and A. M. Ruedin, “Directional Continuous Wavelet Transform Applied to Handwritten Numerals Recognition Using Neural Networks,” Java Caching System, Vol. 7, No. 1, 2007, pp. 66-71.

[29] S.-W. Lee, C.-H. Kim, H. Ma and Y. Y. Tang, “Multiresolution Recognition of Unconstrained Handwritten Numerals with Wavelet Transform and Multilayer Cluster Neural Network,” Pattern Recognition, Vol. 29, No. 12, 1996, pp. 1953-1961. doi:10.1016/S0031-3203(96)00053-2

[30] J. C. Lee, T. J. Fong and Y. F. Chang, “Feature Extraction for Handwritten Chinese Character Recognition Using X-Y Graphs Decomposition and Haar Wavelet,” IEEE International Conference on Signal and Image Processing Applications, 18-19 November 2009, pp. 10-14.

[31] D. Sharma and D. Gupta, “Isolated Handwritten Digit Recognition Using Adaptive Unsupervised Incremental Learning Technique,” International Journal of Computer Applications, Vol. 7, No. 4, 2010, pp. 27-33.

[32] T. Hastie and P. Y. Simard, “Metrics and Models for Handwritten Character Recognition,” Statistical Science, Vol. 13, No. 1, 1998, pp. 54-65. doi:10.1214/ss/1028905973

[33] S. Basu, N. Das, R. Sarkar, M. Kundu, M. Nasipuri and D. K. Basu, “A Hierarchical Approach to Recognition of Handwritten Bangla Characters,” Pattern Recognition, Vol. 42, No. 7, 2009, pp. 1467-1484. doi:10.1016/j.patcog.2009.01.008

[34] C.-L. Liu and C. Y. Suen, “A New Benchmark on the Recognition of Handwritten Bangla and Farsi Numeral Characters,” Pattern Recognition, Vol. 42, No. 12, 2009, pp. 3287-3295. doi:10.1016/j.patcog.2008.10.007

[35] M. E. Stevens, “Introduction to the Special Issue on Optical Character Recognition (OCR),” Pattern Recognition, Vol. 2, No. 3, 1970, pp. 147-150. doi:10.1016/0031-3203(70)90026-9

[36] J. C. Rabinow, “Whither OCR and Whence?” Datamation, 1969, pp. 38-42.