Abstract
Offline Handwritten Character Recognition of Malayalam scripts have gained remarkable attention in the past few years. The complicated writing style of Malayalam characters with loops and curves make the recognition process highly challenging. This paper presents a comparative study of Malayalam character recognition using 4 different feature sets—Zonal features, Projection histograms, Chain code histograms and Histogram of Oriented Gradients. The performance of these features for isolated Malayalam vowels and 5 consonants are evaluated in this study using feedforward neural networks as classifier. The final recognition results were computed using a 5 fold cross validation scheme. The best recognition accuracy of 94.23 % was obtained in this study using Histogram of Oriented Gradients features.
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References
Plamondan, R., Srihari, S.N.: Online and offline character recognition: a comprehensive survey. IEEE Trans. PAMI 22, 63–84 (2000)
Lajish, V.L.: Handwritten character recognition using perpetual fuzzy zoning and class modular neural networks. In: Proceedings of 4th International National Conference on Innovations in IT, pp. 188–192 (2007)
John, R., Raju, G., Guru, D.S.: 1D wavelet transform of projection profiles for isolated handwritten character recognition. In: Proceedings of ICCIMA07, pp. 481–485, Sivakasi (2007)
Raju, G.: Wavelet transform and projection profiles in handwritten character recognition—a performance analysis. In: Proceedings of 16th International Conference on Advanced Computing and Communications, pp. 309–314, Chennai (2008)
John, J., Pramod K.V., Balakrishnan K.: Offline handwritten Malayalam character recognition based on chain code histogram. In: Proceedings of ICETECT (2011)
John, J., Pramod, K.V., Balakrishnan, K.: Unconstrained handwritten Malayalam character recognition using wavelet transform and support vector machine classifier. In: International Conference on Communication Technology and System Design, Elsevier (2011)
Moni, B.S., Raju, G.: Modified quadratic classifier and directional features for handwritten Malayalam character recognition. In: IJCA Special Issue on Computational Science—New Dimensions Perspectives NCCSE (2011)
John, J., Balakrishnan, K., Pramod, K.V.: A system for offline recognition of handwritten characters in Malayalam script. Int. J. Image Graph. Signal Process. 4, 53–59 (2013)
Chacko, A.M.M.O.: Dhanya PM, Handwritten character recognition in Malayalam scripts—a review. Int. J. Artif. Intell. Appl. (IJAIA) 5(1), 79–89 (2014)
Trier, O.D., Jain, A.K., Taxt, J.: Feature extraction methods for character recognition—a survey. Pattern Recogn. 29(4), 641–662 (1996)
Freeman, H.: On the encoding of arbitrary geometric configurations. IRE Trans. Electr. Comp. TC 10(2), 260–268 (1961)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
Ludwig, O., Delgado, D., Goncalves, V., Nunes, U.: Trainable classifier-fusion schemes: an application to pedestrian detection. In: 12th International IEEE Conference on Intelligent Transport Systems, pp. 1–6 (2009)
Chacko, A.M.M.O., Dhanya, P.M.: A differential chain code histogram based approach for offline Malayalam character recognition. In: International Conference on Communication and Computing (ICC-2014), pp. 134–139 (2014)
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Chacko, A.M.M.O., Dhanya, P.M. (2015). A Comparative Study of Different Feature Extraction Techniques for Offline Malayalam Character Recognition. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 2. Smart Innovation, Systems and Technologies, vol 32. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2208-8_2
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DOI: https://doi.org/10.1007/978-81-322-2208-8_2
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