Applying the Histogram of Oriented Gradients to Recognize Arabic Letters
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Date
2021
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Institute of Electrical and Electronics Engineers Inc.
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Abstract
the aim of this paper is to recognize the Arabic handwriting letters by using histogram of oriented gradients (HOG). We collected 2240 letters by 8 people, each person wrote 28 alphabet letter 10 times. First of all we resize All 2240 hand writing letter of Arabic Alphabet as images(pre-processing) after that extract these images by using one of feature extraction methods which is histogram of oriented gradients (HOG).For classification, the K-Nearest Neighbor (KNN) is used. The results are shown by using 1120 images in the one case and 2240 images in the second case and evaluate these results with the confusion matrix. Other cases we used leave one out (LOO), 2-fold classification and leave one out cross validation. The best fully performance of HOG was with leave one out technique because of the ability of HOG algorithm to capture the shape of letter in the image according to its edges (gradients). © 2021 IEEE.
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2-fold classification, Arabic Alphabet, Confusion Matrix, Histogram of Oriented Gradient, K-Nearest Neighbor, leave one out (LOO)
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2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2021 - Proceedings -- 1st IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2021 -- 25 May 2021 through 27 May 2021 -- Tripoli -- 171040
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Start Page
350
End Page
355