Abstract
Even though various advances have been made in recent years, the recognition of handwritten characters is still an open challenge in the Pattern Recognition field. Different approaches are invented for the recognition of printed characters of Indian languages. However, few attempts are done for the recognition of handwritten characters. A high degree of recognition accuracy for the handwritten characters is yet to be achieved. In this paper, a new approach based on deep belief network with the distributed average of gradients feature is presented for the recognition of isolated handwritten characters of Kannada, which is the official language of Karnataka state in India. In the proposed methods, a better accuracy is achieved.
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Mori, S., Suen, C.Y., Yamamoto, K.: Historical review of OCR research and development. IEEE Proc. 80(7), 1029–1057 (1992)
Plamandon, R., Lopresti, D., Schomaker, L.R.B., Srihari, R.: On-line handwriting recognition. In: Webster, J.G. (ed.) Encyclopedia of Electrical and Electronics Engineering, vol. 15, pp. 123–146. Wiley, New York (1999)
Amin, A.: Off-line Arabic character recognition: the state of the Art. Pattern Recogn. 31, 517–530 (1998)
Nagy, G.: Chinese character recognition—a twenty five years retrospective. In: Proceedings of the Ninth International Conference on Pattern Recognition, pp. 109–114 (1988)
Majumdar, A., Chaudhuri, B.B.: A MLP classifier for both printed and handwritten Bangla Numeral Recognition. In: Kalra, P.K., Peleg, S. (eds.) ICVGIP 2006. LNCS, vol. 4338, pp. 796–804. Springer, Heidelberg (2006)
Chaudhuri, B.B., Majumdar, A.: Curvelet-based multi SVM recognizer for offline handwritten Bangla: a major Indian script. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), pp. 491–495 (2007)
M. A. Naser, A. Mahmud, T. M. Arefin, G. Sarowar, M. M. Naushad Ali, Comparative analysis of radon and fan-beam based feature extraction techniques for Bangla character recognition. Int. J. Comput. Sci. Netw. Security (IJCSNS), 9(9), 287-289, 2009
Shrivastava, S.K., Gharde, S.S.: Support vector machine for handwritten Devanagari numeral recognition. Int. J. Comput. Appl. 7(11), 9–14 (2010)
Rajput, G.G., Mali, S.M.: Fourier descriptor based isolated Marathi handwritten numeral recognition. Int. J. Comput. Appl. 10, 9-13 (2010)
Maloo, M., Kale, K.V.: Support vector machine based Gujarati numeral recognition. Int. J. Comput. Sci. Eng. (IJCSE) 3(7), 2595–2600 (2011)
Lehal, G.S., Singh, C.: A post processor for Gurmukhi OCR. Sadhana 27(2002), 99–111 (2002)
Pal, U., Wakabayashi, T., Kimura, F.: A system for off-line Oriya handwritten character recognition using curvature feature. In: 10th IEEE International Conference on Information Technology, pp. 227–229 (2007)
Srinivas, A., Agarwal, A., Rao, C.R.: Telugu character recognition. In: Proceedings of International Conference on Systemics, Cybernetics, and Informatics, Hyderabad, pp. 654–659 (2007)
Suresh, R.M., Arumugam, S.: Fuzzy technique based recognition of handwritten characters. Image Vis. Comput. 25, 230–239 (2007)
Abdul Rahiman, M., Rajasree, M.S.: Printed Malayalam character recognition using backpropagation neural networks. In: Proceedings of IEEE International Advance Computing Conference, Patiala, pp. 1140–1144, March 2009
Sanjeev, R.: Kunte and R.D. Sudhaker Samuel A simple and efficient optical character recognition system for basic symbols in printed Kannada text. Sadhana 32, 521–533 (2007)
Rajaput, G.G., Hangarge, M.: Recognition of isolated handwritten Kannada numerals based on image fusion method. In: PReMI07, LNCS, vol. 4815, pp. 153–160 (2007)
Ragha, L., Sasikumar, M: Adapting moments for handwritten Kannada Kagunita recognition. In: Second International Conference on Machine Learning and Computing, pp. 125–129 (2010)
Abdul Rahiman, M., Rajasree, M.S.: A detailed study and analysis of OCR research in South Indian scripts. In: International Conference on Advances in Recent Technologies in Communication and Computing, pp. 31–38 (2009)
Sheshadri, K., Ambekar, T., Kumar, P., Prasad, D.P., Kumar, R.P.: An OCR system for printed Kannada using K-means clustering. In: 2010 IEEE International Conference on Industrial Technology (ICIT), pp. 183–187 (2010)
Rajput, G.G., Horakeri, R., Chandrakant, S.: Printed and handwritten mixed Kannada numerals recognition using SVM. Int. J. Comput. Sci. Eng. (IJCSE) 2(5), 1622–1626 (2010)
Pal, U., Wakabayashi, T., Sharma, N., Kimura, F.: Handwritten numeral recognition of six popular Indian scripts. In: Proceedings of 9th International Conference on Document Analysis and Recognition, vol. 2, pp. 749–753 (2007)
Suresh, R.M., Arumugam, S.: Fuzzy technique based recognition of handwritten characters. Image Vis. Comput. 25, 230–239 (2007)
Majumdar, A., Chaudhuri, B.B.: A MLP Classifier for both printed and handwritten Bangla numeral recognition. In: Proceedings of ICVGIP 2006. LNCS, vol. 4338, pp. 796–804 (2006)
Chaudhuri, B.B., Majumdar, A.: Curvelet-based Multi SVM recognizer for offline handwritten Bangla: a major Indian script. In: Proceedings of 9th Bibliography, International Conference on Document Analysis and Recognition (ICDAR 2007), vol. 1, pp. 491–495 (2007)
Shrivastava, S.K., Gharde, S.S.: Support vector machine for handwritten Devanagari numeral recognition. Int. J. Comput. Appl. 7(11), 9–14 (2010)
Arora, S., Bhattacharjee, D., Nasipuri, M., Basu, D.K., Kundu, M.: Combining multiple feature extraction techniques for handwritten Devanagari character recognition. In: Proceedings of IEEE Region 10 and the Third international Conference on Industrial and Information Systems (ICIIS 2008), pp. 342/1-6, 8–10 December 2008
Pal, U., Wakabayashi, T., Kimura, F.: Comparative study of Devanagari handwritten character recognition using different feature and classifiers. In: Proceedings of 10th International Conference on Document Analysis and Recognition, pp. 1111–1115 (2009)
Lehal, G.S., Singh, C.: A post processor for Gurmukhi OCR. Sadhana 27(Part 1), 99–111 (2002)
Srinivas, A., Agarwal, A., Rao, C.R.: Telugu character recognition. In: Proceedings of International Conference on Systemics, Cybernetics, and Informatics, Hyderabad, pp. 654–659 (2007)
Seethalakshmi, R., Sreeranjani, T.R., Balachandar, T., Singh, A., Singh, M., Ratan, R., Kumar, S.: Optical character recognition for printed Tamil text using unicode. J. Zhejiang Univ. SCI 6A(11), 1297–1305 (2005)
Kunte, R.S., Samuel, R.D.S.: A simple and efficient optical character recognition system for basic symbols in printed Kannada text. Sadhana 32(Part 5), 521–533 (2007)
Rajashekararadhya, S.V., Ranjan, P.V.: Support vector machine based handwritten numeral recognition of Kannada script. In: Proceedings of 2009 IEEE International Advance Computing Conference (IACC 2009) Patiala, India, pp. 381–386, 6–7 March 2009
Rajput, G.G., Horakeri, R., Chandrakant, S.: Printed and handwritten mixed Kannada numerals recognition using SVM. Int. J. Comput. Sci. Eng. 2(5), 1622–1626 (2010)
Sandhya, N., Krishnan, R.: Broken Kannada character recognition—a neural network based approach. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)—2016, pp. 2047–2050 (2016)
Sushma, A., Veena, G.S.: Kannada handwritten word conversion to electronic textual format using HMM model. In: International Conference on Computational Systems and Information Systems for Sustainable Solutions, pp. 330–335 (2016)
Gowda, P.K., Chethan, S., Harsha, J., Rakesh, J., Tanushree, K.N.: Offline Kannada handwritten word recognition using locality preserving projections (LPP). Int. J. Innov. Res. Comput. Commun. Eng. 5(5), 9955–9960 (2017)
Mamatha, H.R., Srikanta Murthy, K., Sudan, S., Raj, V.G., Jois, S.S.: Fan beam projection based features to recognize handwritten Kannada numerals. In: International Conference on Software and Computer Applications (2011)
Mamatha, H.R., Srirangaprasad, S., Srikantamurthy, K.: Recognition of isolated handwritten Kannada numerals based on decision fusion approach. Int. J. Comput. Sci. Eng. 5(6), 465–473 (2013)
Hinton, G.: NIPS Tutorial On: Deep Belief Nets. Canadian Institute for Advanced Research & Department of Computer Science, University of Toronto, Toronto (2007)
Deng, L., Yu, D.: Deep learning methods and applications, foundations and trends. Signal Process. 7(3–4), 242–247 (2013)
Hossein Mirabdollah, M., Mohamed, M.A., Mertsching, B.: Distributed average of gradients: a fast alternative for histogram of oriented gradients. In: 20th Robocup International Symposium, Germany (20160
Pal, U., Sharma, N., Wakabayashi, T., Kimura, F.: Handwritten Character Recognition of Popular South Indian Scripts. In: Doermann, D., Jaeger, S. (eds.) SACH 2006. LNCS, vol. 4768, pp. 251–264 (2008)
Sangame, S.K., Ramteke, R.J., Yogesh, V.G.: Recognition of isolated handwritten Kannada characters using invariant moments and chain code. World J. Sci. Technol. 1(8), 115–120 (2011)
Sangame, S.K., Ramteke, R.J., Benne, R.: Recognition of isolated handwritten Kannada vowels. Adv. Comput. Res. 1(2), 52–55 (2009)
Karthik, S., Srikanta Murthy, K.: Handwritten Kannada characters recognition using BRISK descriptors. In: International Conference on Innovations in Computer Science & Information Technology (ICICSIT) (2015)
Karthik, S., Srikanta Murthy, K.: Deep belief network based approach to recognize handwritten Kannada characters using histogram of oriented gradients and raw pixel features. Int. J. Appl. Eng. Res. 10(5), 3979–3982 (2015)
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Karthik, S., Srikanta Murthy, K. Deep belief network based approach to recognize handwritten Kannada characters using distributed average of gradients. Cluster Comput 22 (Suppl 2), 4673–4681 (2019). https://doi.org/10.1007/s10586-018-2274-0
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DOI: https://doi.org/10.1007/s10586-018-2274-0