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    C-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques

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    Date
    2022
    Author
    Hamdi, Ali
    Al-Nuzaili, Qais
    Ghaleb, Fuad A.
    Shaban, Khaled
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    Abstract
    We propose C-SAR, a Class-specific and Adaptive Recognition algorithm for Arabic handwritten Cheques. Existing methods suffer from low accuracy due to the complex structure of Arabic script and high-dimensional datasets. In this paper, we present an adaptive algorithm that implements a class-specific classification to address these challenging issues. C-SAR trains a set of class-specific machine learning models of Support Vector Machines and Artificial Neural Networks features extracted using angular pixel distribution approach. Furthermore, we propose a class-specific taxonomy of Arabic cheque handwritten words. The proposed taxonomy divides the Arabic words into groups over three layers based on their structural characteristics. Accordingly, C-SAR performs classification on three phases, i.e., 1) similar and non-similar structures, for binary classification, 2) classes with similar structures into another two categories, and 3) class-specific models to recognize the Arabic word from the given image. We introduce benchmark experimental results of our method against previous methods on the Arabic Handwriting Database for Text Recognition. Our method outperforms the baseline methods with at least 5% accuracy having 90% average classification accuracy. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
    DOI/handle
    http://dx.doi.org/10.1007/978-3-030-98741-1_17
    http://hdl.handle.net/10576/37499
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