Abstract
Arabic characters illustrate intricate, multidimensional and cursive visual information. Developing a machine learning system for Arabic character recognition is an exciting research. This paper addresses a neural computing concept for Arabic Optical Character Recognition (OCR). The method is based on local image sampling of each character to a selected feature matrix and feeding these matrices into a Bidirectional Associative Memory followed by Multilayer Perceptron (BAMMLP) with back propagation learning algorithm. The efficacy of the system has been justified over different test patterns of Arabic characters. Experimental results validate that the system is well efficient to recognize Arabic characters with overall more than 82% accuracy.
Highlights
Arabic language occupies a significant role in mass communication
This paper proposes a BAMMLP approach for Arabic character recognition that is commenced on local image sampling by converting each Arabic character into a selected M×N feature matrix
The remainder of the article is organized as: Section II describes salient features of Arabic scripts, Section III describes the proposed Arabic Optical Character Recognition (OCR) algorithm, Section IV highlights the architecture of BAMMLP network, Section V outlines the experimental results, and the conclusion section outlines the overall conclusions of the article
Summary
Arabic language occupies a significant role in mass communication. Over 200 million people speak in Arabic language as mother tongue [1], and more than one billion people exercise it for multifarious religion-oriented matters. Vaseghi et al [16] presented a holistic approach to recognize handwritten Farsic/Arabic word employing discrete Markov chain and Kohonen feature map for Arabic character recognition. Supriana and Nasution [20] have implemented binarization and median filter for Arabic character recognition They employed Hilditch operator for thinning combined by two templates, one to prevent redundant tail and the other one to eliminate redundant interest points. Al-Helali and Mahmoud [23] have processed the delayed strokes of Arabic characters and proposed a framework for Arabic character recognition They evaluated the statistical features of Arabic characters but they did not consider the connectivity problems, variability, and style change of text. The remainder of the article is organized as: Section II describes salient features of Arabic scripts, Section III describes the proposed Arabic OCR algorithm, Section IV highlights the architecture of BAMMLP network, Section V outlines the experimental results, and the conclusion section outlines the overall conclusions of the article
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