A Gabor filters based method for segmenting inflected characters of Kannada script

OCR system plays an important role in automatic identification of a script in a given document image, which provides important applications. A country like India, most of the people use more than one language in their day to day life; the requirement of OCR system is very much essential. There is not much work in developing OCR system for south Indian languages such as Kannada are reported in the literature. Recognition of the Kannada character is more complex and challenging, because it has large set of character with more similarity in properties among characters and characters belonging to same class have higher variability among different set of fonts. Moreover, the Kannada characters are formed by combination of basic symbols; a natural approach for recognition is to segment characters into basic symbol and recognize each symbol subsequently. Therefore a character level segmentation method is highly desirable. Also precise segmentation will certainly reduce the number of classes to recognize. The naïve use of characters images for segmentation may not yield more accurate results. With previous studies which have confirmed that the multi-channel Gabor decomposition represents an excellent tool for image segmentation and texture analysis, we propose a novel character segmentation method using Gabor filters. On comparing with manually segmented benchmark data we obtained overall accuracy of 93.82%.