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Gabor features for offline Arabic handwriting recognition by Jin Chen; Huaigu Cao; Rohit Prasad; Anurag Bhardwaj; Prem Natarajan is a scholarly article available to read on EtoBox.

What is Gabor features for offline Arabic handwriting recognition about?

Many feature extraction approaches for off-line handwriting recognition (OHR) rely on accurate binarization of graylevel images. However, high-quality binarization of most real-world documents is extremely difficult due to varying characteristics of noises artifacts common in such documents. Unlike most of these features, Gabor features do not require binarization of the document images, and thus are likely to be more robust to noises in document images. To demonstrate the efficacy of our proposed Gabor features, we perform subword recognition for off-line Arabic handwritten images using Support Vector Machines (SVM). We also compare the recognition performance with other binarization based features which have been proven to be effective in capturing shape characteristics of handwritten Arabic subwords, such as GSC (a set of gradient, structure, and concavity features) and skeleton based Graph features. Our preliminary experimental results show that Gabor features outperform Graph features and are slightly better than GSC features for Arabic subword recognition. In addition, by combining Gabor and GSC features, we obtain a significant reduction in classification error rate over usi

Author

Jin Chen; Huaigu Cao; Rohit Prasad; Anurag Bhardwaj; Prem Natarajan

Publisher

ACM

Published

2010

Language

EN