Performance Comparison of Gurmukhi Script: k-NN Classifier with DCT and Gabor Filter

This paper presents a comparative performance analysis for Gurmukhi OCR at word level. To evaluate the performance k¬NN classifier has been used. Before the classification, Features have been extracted from word images. For feature extraction, word images have been scanned and these images are machi...

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Bibliographic Details
Published in:International journal of advanced research in computer science 2017-05, Vol.8 (5), p.762
Main Authors: Dhiman, Sapna, Gurpreet Singh Lehal
Format: Article
Language:English
Subjects:
Discrete cosine transform
Feature extraction
Image classification
Performance evaluation
Online Access:Get full text
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Summary:This paper presents a comparative performance analysis for Gurmukhi OCR at word level. To evaluate the performance k¬NN classifier has been used. Before the classification, Features have been extracted from word images. For feature extraction, word images have been scanned and these images are machine printed images.Here Discrete Cosine Transform (DCT) and Gabor filter has been used to extract the features. DCT provides 100 features of scanned images in zigzag method and Gobor provides 189 features for scanned images. To train the classifier of Gurmukhi OCR, 50 different classes with 3035 samples of each class i.e 1600 samples have been taken. 750 samples have been used to test the system. Using Gabor filter, kNN classifier provides 92.6229%of correctness while with DCT with kNN provides 96.9945% of accuracy.
ISSN:0976-5697
DOI:10.26483/ijarcs.v8i5.3414