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AuthorHamdi, Ali
AuthorAl-Nuzaili, Qais
AuthorGhaleb, Fuad A.
AuthorShaban, Khaled
Available date2022-12-21T10:01:46Z
Publication Date2022
Publication NameLecture Notes on Data Engineering and Communications Technologies
ResourceScopus
URIhttp://dx.doi.org/10.1007/978-3-030-98741-1_17
URIhttp://hdl.handle.net/10576/37499
AbstractWe 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.
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectHandwritten recognition
Image classification
TitleC-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques
TypeBook chapter
Pagination193-208
Volume Number127
dc.accessType Abstract Only


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