Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 1 Curvelet-Based Multi SVM Recognizer for Offline Handwritten Bangla: A Major Indian Script Curitiba, Parana, Brazil September 23-September 26 ISBN: 0-7695-2822-8
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDAR.2007.95
This paper deals with automatic recognition of offline handwritten Bangla characters. Bangla is the second most popular script among SAARC countries. A new class of features based on Curvelet transform has been used in our classification scheme. The classifier used was SVM with one-against-rest class model. The training and test set were morphologically deformed to get five versions of the same character and each version has been subject to individual SVM classifier. Five classifier outputs obtained in this way have been combined by simple majority voting scheme. The overall recognition accuracy of 95.5% has been obtained on the data set. It is hoped that the Curvelet transform along with such multi-classifier scheme will be useful in other handwritten character data as well.
Citation:
A. Majumdar, B.B. Chaudhuri, "Curvelet-Based Multi SVM Recognizer for Offline Handwritten Bangla: A Major Indian Script," icdar, vol. 1, pp.491-495, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 1, 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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