Performance Evaluation of GMM and SVM for Recognition of Hierarchical Clustering Character
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.
KeywordsHandwritten Character Recognition Character Intensity Vector(CIV) Hierarchical Character Clustering Support Vector Machine(SVM) Gaussian Mixture Model(GMM)
Unable to display preview. Download preview PDF.
- 1.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)Google Scholar
- 8.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)Google Scholar
- 11.Reynolds, D.A.: Gaussian Mixture Models. MIT Lincoln Laboratory, USAGoogle Scholar
- 13.Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press (2000)Google Scholar
- 14.Mitchell, T.: Machine Learning. Hill Computer Science Series (1997)Google Scholar