Abstract
Kannada script has large number of characters with similar looking shapes among characters and characters belonging to same class have higher variability across different set of fonts. Moreover, the Kannada characters are formed by combination of basic symbols, recognition of the Kannada character is complex and challenging task. The better approach for recognition is to segment characters into basic symbol and recognize each symbol subsequently. Therefore a character level segmentation method is highly desirable. In the recent years it is found that the multi-channel Gabor decomposition represents an excellent tool for image segmentation and texture analysis. At higher frequency, Gabor filters have property to extract edge information. By analyzing such responses we have proposed a novel character segmentation method to segment top vowel modifier portion from an akshara (analogous to characters in English). Experiments are conducted on benchmark database of 1088 samples. Overall accuracy of 96.87% for top row index and 95.49% for consonant row index is observed.
Published Version
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