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

Hamdi, Ali and Al-Nuzaili, Qais and Ghaleb, Fuad A. and Shaban, Khaled (2022) C-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques. Lecture Notes on Data Engineering and Communications Technologies, 127 (NA). pp. 193-208. ISSN 2367-4512

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Official URL: http://dx.doi.org/10.1007/978-3-030-98741-1_17

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.

Item Type:Article
Uncontrolled Keywords:Handwritten recognition, Image classification
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:100311
Deposited By: Widya Wahid
Deposited On:29 Mar 2023 07:47
Last Modified:29 Mar 2023 07:47

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