A comparison of Pattern Recognition Approaches for Recognizing Handwriting in Arabic Letters

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2021

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Institute of Electrical and Electronics Engineers Inc.

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Computer Engineering
(1998)
The Atılım University Department of Computer Engineering was founded in 1998. The department curriculum is prepared in a way that meets the demands for knowledge and skills after graduation, and is subject to periodical reviews and updates in line with international standards. Our Department offers education in many fields of expertise, such as software development, hardware systems, data structures, computer networks, artificial intelligence, machine learning, image processing, natural language processing, object based design, information security, and cloud computing. The education offered by our department is based on practical approaches, with modern laboratories, projects and internship programs. The undergraduate program at our department was accredited in 2014 by the Association of Evaluation and Accreditation of Engineering Programs (MÜDEK) and was granted the label EUR-ACE, valid through Europe. In addition to the undergraduate program, our department offers thesis or non-thesis graduate degree programs (MS).

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Abstract

For Arabic letters recognition, we achieve three of pattern recognition approaches namely gray level co-occurrence matrix (GLCM), local binary pattern recognition (LBP) and artificial neural network (ANN) and compare between them to result best performance. Two of these methods level co-occurrence matrix and local binary pattern recognition are used for feature extraction whereas in artificial neural network (ANN) we use the intensity values of pixels for input of the neural network. Two classifiers are used, the K-Nearest Neighbor classifier (KNN) for the LBP, GLCM and neural network classifier for (ANN) artificial neural network. Also, we evaluate the results by using leave one person out approach, fold classification and leave one out. © 2021 IEEE.

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Arabic Alphabet, Artificial Neural Network, Gray Level Co-occurrence Matrix, K-Nearest Neighbor classifier (KNN), Local Binary Pattern

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2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2021 - Proceedings -- 1st IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2021 -- 25 May 2021 through 27 May 2021 -- Tripoli -- 171040

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Start Page

818

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824

URI

https://doi.org/10.1109/MI-STA52233.2021.9464483
 https://hdl.handle.net/20.500.14411/3999

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Scopus