Recognising handwritten Arabic manuscripts using a single hidden Markov model
Pattern Recognition Letters
Offline recognition of omnifont Arabic text using the HMM ToolKit (HTK)
Pattern Recognition Letters
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ECC'09 Proceedings of the 3rd international conference on European computing conference
HMM-based system for recognizing words in historical Arabic manuscript
International Journal of Robotics and Automation
Automatic processing of Arabic text
IIT'09 Proceedings of the 6th international conference on Innovations in information technology
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
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In this paper, we present a new technique for recognizing Arabic cursive words from scanned images of text. The approach is segmentation-free, and is applied to four different Arabic typefaces, where ligatures and overlaps pose challenges to segmentation-based methods. We transform each word into a normalized polar image, and then we apply a two-dimensional Fourier transform to the polar image. The resultant spectrum tolerates variations in size, rotation or displacement. A template that includes a set of Fourier coefficients represents each word. The recognition is based on a normalized Euclidean distance from those tem-plates.