DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-3, Issue-2 ( Feb, 2015 ) | |||||||||
Paper Title |
Comparison Of SVM And K-Nn Classifiers For Recognizing Degraded Printed Gurmukhi Numerals | |||||||||
Author Name |
Seema, Nishu Goyal | |||||||||
Affilition |
Assistant Professor (Computer Engineering), Yadavindra College of Engineering, Punjabi University Guru Kashi Campus, Talwandi Sabo, Bathinda (Punjab) India M.Tech. Student (Computer Engineering) Yadavindra College of Engineering, Punjabi University Guru Kashi Campus, Talwandi Sabo, Bathinda (Punjab) India | |||||||||
Pages |
44-48 | |||||||||
Abstract |
Character recognition is very important areas in the field of document analysis and recognition. Character recognition can be performed on fine printed, handwritten or typewritten text. The accuracy and performance of optical character recognition system depends on the printing quality of the text document. Various researchers have done prominent work in recognition of printed and handwritten text using OCR. Limited authors have worked for recognition of degraded Gurmukhi Numerals using OCR. In present paper, the main focus is to recognize printed degraded Gurmukhi Numerals using OCR. Binarization technique was applied to recognize degraded printed Gurmukhi numerals. Different types of printed degradations such as broken characters, background noise problem heavily printed and shape variant characters were considered during recognition of degraded Gurmukhi numerals. Various structural and statistical features e.g. zoning, transition features, distance profile features and neighbor pixel zone, were used for generating feature sets to recognize printed Gurmukhi numerals using support vector machine (SVM) and k-nearest neighbors’ (K-NN) method. Normalized accuracy and error are calculated to evaluate the performance of various recognition techniques and their combination in various training/tested case study formats using SVM and K-NN classifiers. Keywords: OCR, Degraded Gurmukhi numerals, Feature extraction, Zoning, Classifer, Support vector machine, K-nearest neighbors. | |||||||||
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