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A Hybrid Approach to Classify Gurmukhi Script Characters

Researchers have worked extensively on OCR, in the past few decades. This is also visible from the fact that various types of OCR are available in the market. Out of these available OCR's majority is to support foreign languages. In Indian context, majority of available OCR's are for Hindi and Bangla, but a very few reports are available on Gurmukhi script which is used to write Punjabi language, one of the popular languages of northern India. OCR system consists of image acquisition, the preprocessing, the segmentation, the feature extraction and the classification. Out of these, segmentation is most crucial process. The degree of accuracy of this process leads to the success or failure of OCR system. In the present study, a hybrid scheme is proposed for Gurmukhi script. As per this scheme, we used the concept of water reservoir as well as Feature Extraction. This combination is tried for Gurmukhi script. With the proposed approach, Gurmukhi script characters are classified in 11 subclasses and 9 individual characters. To identify the class of one character, one is to go with maximum 7 checks only. This reduces the efforts, to