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Original scientific paper
https://doi.org/10.2498/cit.2005.03.04

Arabic Font Recognition Using Decision Trees Built From Common Words

Ibrahim S. I. Abuhaiba

Fulltext: english, pdf (394 KB) pages 211-224 downloads: 950* cite
APA 6th Edition
Abuhaiba, I.S.I. (2005). Arabic Font Recognition Using Decision Trees Built From Common Words. Journal of computing and information technology, 13 (3), 211-224. https://doi.org/10.2498/cit.2005.03.04
MLA 8th Edition
Abuhaiba, Ibrahim S. I.. "Arabic Font Recognition Using Decision Trees Built From Common Words." Journal of computing and information technology, vol. 13, no. 3, 2005, pp. 211-224. https://doi.org/10.2498/cit.2005.03.04. Accessed 11 Apr. 2021.
Chicago 17th Edition
Abuhaiba, Ibrahim S. I.. "Arabic Font Recognition Using Decision Trees Built From Common Words." Journal of computing and information technology 13, no. 3 (2005): 211-224. https://doi.org/10.2498/cit.2005.03.04
Harvard
Abuhaiba, I.S.I. (2005). 'Arabic Font Recognition Using Decision Trees Built From Common Words', Journal of computing and information technology, 13(3), pp. 211-224. https://doi.org/10.2498/cit.2005.03.04
Vancouver
Abuhaiba ISI. Arabic Font Recognition Using Decision Trees Built From Common Words. Journal of computing and information technology [Internet]. 2005 [cited 2021 April 11];13(3):211-224. https://doi.org/10.2498/cit.2005.03.04
IEEE
I.S.I. Abuhaiba, "Arabic Font Recognition Using Decision Trees Built From Common Words", Journal of computing and information technology, vol.13, no. 3, pp. 211-224, 2005. [Online]. https://doi.org/10.2498/cit.2005.03.04

Abstracts
We present an algorithm for a priori Arabic optical Font Recognition (AFR). The basic idea is to recognize fonts of some common Arabic words. Once these fonts are known, they can be generalized to lines, paragraphs, or neighbor non-common words since these components of a textual material almost have the same font. A decision tree is our approach to recognize Arabic fonts. A set of 48 features is used to learn the tree. These features include horizontal projections, Walsh coefficients, invariant moments, and geometrical attributes. A set of 36 fonts is investigated. The overall success rate is 90.8%. Some fonts show 100% success rate. The average time required to recognize the word font is approximately 0.30 seconds.

Hrčak ID: 44688

URI
https://hrcak.srce.hr/44688

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