Abstract
Text segmentation is an essential pre-processing step for many methods of recognition and for spotting systems as well. There are some characteristics in Arabic that differentiates it from Latin-based scripts. In this thesis proposal, we address the challenges of segmenting offline Arabic handwritten text. Our proposed approach of text segmentaion utilizes the knowledge of Arabic writing. Furthermore, a method for touching segmentation is proposed. To facilitate touching segmentation, a new learning-based baseline estimation method is introduced.
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Jamal, A.T., Suen, C.Y. (2013). Shape-Based Analysis for Automatic Segmentation of Arabic Handwritten Text. In: Zaïane, O.R., Zilles, S. (eds) Advances in Artificial Intelligence. Canadian AI 2013. Lecture Notes in Computer Science(), vol 7884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38457-8_35
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DOI: https://doi.org/10.1007/978-3-642-38457-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38456-1
Online ISBN: 978-3-642-38457-8
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