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Research Article Open Access
Offline Handwritten Word Recognition (HWR) plays a major role in the field of image processing and pattern recognition. Compared to online recognition, handwritten words cannot be identified easily because of the variations in the handwriting styles, type of paper used, quality of the scanner etc. In our paper we have focused on the Kannada handwritten word recognition. Large number of characters present in the Kannada language makes it as a open problem for the researchers. Major steps in offline Kannada HWR are preprocessing, feature extraction, and classification. Locality Preserving Projections (LPP) method is used here for the feature extraction. For the classification Support Vector Machines (SVM) is used. Result is compared with the K-Means classifier. Experimental results show that SVM is better than K-Means classifier for our data set.
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Author(s): M.S. Patel, Rohith Kumar, S.C. Linga Reddy
Offline Handwritten Word Recognition (HWR), preprocessing, feature extraction, classification, Locality Preserving Projections (LPP), Support Vector Machines (SVM), K-Means classifier, Fluid Dynamics