This article proposes an un-constrained recognition approach for the handwritten Arabic script. The approach starts by explicitly segment each word image into its constituent letters, then a filter-bank of Gabor wavelet transform is used to extract feature vectors corresponding to different scales and orientation in the segmented image. Classification is carried out by employing a support vectors machine algorithm, where IESK-arDB and IFN/ENIT databases are used for testing and evaluation of the proposed approach respectively. A Leave-one-out estimation strategy is followed to assess performance, where results confirmed the approach efficiency.
CITATION STYLE
Elzobi, M., Al-Hamadi, A., Al Aghbari, Z., Dings, L., & Saeed, A. (2014). Gabor wavelet recognition approach for off-line handwritten Arabic using explicit segmentation. In Advances in Intelligent Systems and Computing (Vol. 233, pp. 245–254). Springer Verlag. https://doi.org/10.1007/978-3-319-01622-1_29
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