Abstract
This paper presents an approach for performance evaluation of hierarchical clustering character and recognition of handwritten characters. The approach uses as an efficient feature called Character Intensity Vector. A hierarchical recognition methodology based on the structural details of the character is adopted. At the first level similar structured characters are grouped together and the second level is used for individual character recognition. Gaussian Mixture Model and Support Vector Machine are used in first level and second level classifiers and evaluate the accuracy performance of the handwritten characters. Gaussian Mixture Model is used for classification which achieves an overall accuracy of character level 94.39% and Support Vector Machine which achieves an overall accuracy of character level 93.61% is achieved.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Choudhary, A., Rishi, R., Ahlawat, S.: Offline Handwritten Character Recognition using Features Extracted from Binarization Technique. In: AASRI Conference on Intelligent Systems and Control, pp. 306–312 (2013)
Vamvakas, G., Gatos, B., Perantonis, S.J.: Handwritten Character Recognition Through Two-Stage Foreground Sub-Sampling. Pattern Recognition 43, 2807–2816 (2010)
Lei, L., Li-liang, Z., Jing-fei, S.: Handwritten Character Recognition via Direction String and Nearest Neighbour Matching. The Journal of China Universities of Posts and Telecommunications 19(2), 160–165 (2012)
Pirlo, G., Impedovo, D.: Adaptive Membership Functions for Handwritten Character Recognition by Voronoi Based Image Zoning. IEEE Transactions on Image Processing 21(9), 3827–3837 (2012)
Pirlo, G., Impedovo, D.: Fuzzy Zoning Based Classification for Handwritten Characters. IEEE Transactions on Fuzzy Systems 19(4), 780–785 (2011)
Das, N., Reddy, J.M., Sarkar, R., Basu, S., Kundu, M., Nasipuri, M., Basu, D.K.: A Statistical Topological Feature Combination for Recognition of Handwritten Numerals. Applied Soft Computing 12, 2486–2495 (2012)
Pauplin, O., Jiang, J.: DBN-Based Structural Learning and Optimisation for Automated Handwritten Character Recognition. Pattern Recognition Letters 33, 685–692 (2012)
Reza, K.N., Khan, M.: Grouping of Handwritten Bangla Basic Characters, Numerals and Vowel Modifiers for Multilayer Classification. In: ICFHR 2012 Proceedings of International Conference on Frontiers in Handwriting Recognition, pp. 325–330 (2012)
Bhowmik, T.K., Ghanty, P., Roy, A., Parui, S.K.: SVM based Hierarchical Architectures for Handwritten Bangala Character Recognition. International Journal on Document Analysis and Recognition (IJDAR) 12, 97–108 (2009)
Bharathi, V.C., Geetha, M.K.: Segregated Handwritten Character Recognition using GLCM Features. International Journal of Computer Applications 84(2), 1–7 (2013)
Reynolds, D.A.: Gaussian Mixture Models. MIT Lincoln Laboratory, USA
Sarmah, K., Bhattacharjee, U.: GMM based Language Identification using MFCC and SDC Features. International Journal of Computer Applications 85(5), 36–42 (2014)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press (2000)
Mitchell, T.: Machine Learning. Hill Computer Science Series (1997)
Vapnik, V.: Statistical Learning Theory. Wiley, NY (1998)
Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 1–27 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bharathi, V.C., Kalaiselvi Geetha, M. (2014). Performance Evaluation of GMM and SVM for Recognition of Hierarchical Clustering Character. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 1. Smart Innovation, Systems and Technologies, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-07353-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-07353-8_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07352-1
Online ISBN: 978-3-319-07353-8
eBook Packages: EngineeringEngineering (R0)