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Recognition of Handwritten Arabic Characters using Histograms of Oriented Gradient (HOG)

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Abstract

Optical Character Recognition (OCR) is the process of recognizing printed or handwritten text on paper documents. This paper proposes an OCR system for Arabic characters. In addition to the preprocessing phase, the proposed recognition system consists mainly of three phases. In the first phase, we employ word segmentation to extract characters. In the second phase, Histograms of Oriented Gradient (HOG) are used for feature extraction. The final phase employs Support Vector Machine (SVM) for classifying characters. We have applied the proposed method for the recognition of Jordanian city, town, and village names as a case study, in addition to many other words that offers the characters shapes that are not covered with Jordan cites. The set has carefully been selected to include every Arabic character in its all four forms. To this end, we have built our own dataset consisting of more than 43.000 handwritten Arabic words (30000 used in the training stage and 13000 used in the testing stage). Experimental results showed a great success of our recognition method compared to the state of the art techniques, where we could achieve very high recognition rates exceeding 99%.

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Author information

Authors and Affiliations

  1. Computer Sciences Department, King Faisal University, Hasa, 31982, Saudi Arabia

    Noor A. Jebril

  2. Computer Engineering Department, Yarmouk University, Irbid, 21163, Jordan

    Hussein R. Al-Zoubi

  3. Electrical Engineering Department., King Faisal University, Hasa, 31982, Saudi Arabia

    Qasem Abu Al-Haija

Authors
  1. Noor A. Jebril
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  2. Hussein R. Al-Zoubi
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  3. Qasem Abu Al-Haija
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Correspondence to Noor A. Jebril.

Additional information

The article is published in the original.

Hussein R. Al-Zoubi received his MSE and Ph.D. in Computer Engineering from the University of Alabama in Huntsville, USA in 2004 and 2007, respectively. Since 2007, he has been working with the Department of Computer Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Jordan. He is currently an associate professor and the chair of the Department of Computer Engineering. His research interests include machine vision, pattern recognition, image processing, computer networks and their applications: wireless and wired, security, multimedia, queuing analysis, and high-speed networks. He is a senior member of IEEE.

Qasem Abu Al-Haija is a senior lecturer of Electrical and Computer Engineering at King Faisal University. He is a Jordanian resident (Married) born on July-16-1982 and proficient in both languages Arabic and English. Eng. Abu Al-Haija received his B.S. in ECE from Mu’tah University in Feb/2005. Then he worked as a network engineer in a leading institute at KSA as well as a lecturer before he joined the graduate program at Jordan University of Science and Technology in Sept/2007 where received his M.Sc. degree in Computer engineering in Dec/2009. His research Interests (Keywords): Information Security and Cryptography, High performance coprocessor and FPGA design, Computer arithmetic and algorithms, Wireless sensor networks.

Noor A. Jebril is a lecturer of Computer Sciences at King Faisal University. She is a Jordanian resident (Married) proficient in both languages Arabic and English. Eng. Noor received his B.S. in software engineering from Philadelphia University in Aug/2009. She joined the graduate program at Yarmouk University of Science and Technology in Aug/2011 where she received her M.Sc. degree in computer engineering before she joins the faculty staff at King Faisal university as a lecturer of computer science department. Here research interests include: Digital pmage processing, Biomedical hardware and software, Computer programming, algorithms and security.

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Jebril, N.A., Al-Zoubi, H.R. & Abu Al-Haija, Q. Recognition of Handwritten Arabic Characters using Histograms of Oriented Gradient (HOG). Pattern Recognit. Image Anal. 28, 321–345 (2018). https://doi.org/10.1134/S1054661818020141

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  • DOI: https://doi.org/10.1134/S1054661818020141

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