Deep Learning Algorithms for Arabic Handwriting Recognition: A Review

  • Authors

    • Ahmed AL-Saffar
    • Suryanti Awang
    • Wafaa AL-Saiagh
    • Sabrina Tiun
    • A S. Al-khaleefa
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.20.19271
  • Arabic OCR, Deep Convolutional Neural Networks, pattern recognition, image processing, Text recognition.

  • Abstract

     Computer vision (CV) refers to the study of the computer simulation of human visual science. Major task of CV is to collect images (or video) so that they could be used for analysis, gathering information, and making decisions or judgements. CV has greatly progressed and developed in the past few decades. In recent years, deep learning (DL) approaches have won several contests in pattern recognition and machine learning. (DL) dramatically improved the state-of-the-art in visual object recognition, object detection, handwritten recognition and many other domains. Handwritten recognition technique is one of this tasks that targeted to extract the text from documents or another images type. In contrast to the English domain, there are a limited works on the Arabic language that achieved satisfactory results, Due to the Arabic language cursive nature that induces many technical difficulties. This paper highlighted the pre-processing and binarization methods that have been used in the literature along with proposed numerous directions for developing. We review the various current deep learning approaches and tools used for Arabic handwritten recognition (AHWR), identified challenges along this line of this research, and gives several recommendations including a framework based (DL) that is particularly applicable for dealing with cursive nature languages.


     

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  • How to Cite

    AL-Saffar, A., Awang, S., AL-Saiagh, W., Tiun, S., & S. Al-khaleefa, A. (2018). Deep Learning Algorithms for Arabic Handwriting Recognition: A Review. International Journal of Engineering & Technology, 7(3.20), 344-353. https://doi.org/10.14419/ijet.v7i3.20.19271

    Received date: 2018-09-08

    Accepted date: 2018-09-08

    Published date: 2018-09-01