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2008

Recognition of off-line printed Arabic text using Hidden Markov Models

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Recognition of off-line printed Arabic text using Hidden Markov Models
This paper describes a technique for automatic recognition of off-line printed Arabic text using Hidden Markov Models. In this work different sizes of overlapping and non-overlapping hierarchical windows are used to generate 16 features from each vertical sliding strip. Eight different Arabic fonts were used for testing (viz. Arial, Tahoma, Akhbar, Thuluth, Naskh, Simplified Arabic, Andalus, and Traditional Arabic). It was experimentally proven that different fonts have their highest recognition rates at different numbers of states (5 or 7) and codebook sizes (128 or 256). Arabic text is cursive, and each character may have up to 4 different shapes based on its location in a word. This research work considered each shape as a different class resulting in a total of 126 classes (compared to 28 Arabic letters). The achieved average recognition rates were between 98.08% and 99.89% for the eight experimental fonts. The main contributions of this work are the novel hierarchical sliding win...
Husni A. Al-Muhtaseb, Sabri A. Mahmoud, Rami Qahwa
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where SIGPRO
Authors Husni A. Al-Muhtaseb, Sabri A. Mahmoud, Rami Qahwaji
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