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Deep Learning Architectures Applied on Arabic-Adapted Scripts: A Review

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Advances in Data and Information Sciences

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 522))

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Abstract

In recent years, machine learning algorithms have been replaced by deep learning in various fields such as computer vision, machine translation, natural language processing, and speech recognition. Deep learning methods in recent times have been extremely successful in developing optical character recognition systems. Deep learning methods can learn directly from raw data. Therefore, they can be perfectly applied for recognition of text from images. Notably, convolutional neural networks and recurrent neural networks are significantly employed for text recognition and word spotting. In this article, the most important techniques of deep learning used in the recognition Arabic-adapted scripts such as Urdu, Arabic, and Pashto have been summarized and compared.

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Authors and Affiliations

  1. Department of Computer Science, Punjabi University Patiala, Patiala, 147002, India

    Maajid Bashir & Vishal Goyal

  2. Department of Computer Sciences, Islamic University of Science and Technology, Awantipora, J &K, India

    Kaiser J. Giri

Authors
  1. Maajid Bashir
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  2. Vishal Goyal
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  3. Kaiser J. Giri
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Corresponding author

Correspondence to Maajid Bashir .

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Editors and Affiliations

  1. Krishna Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  2. Associate Professor, CSED, National Institute of Technology Agartala, Tripura, India

    Munesh C. Trivedi

  3. Faculty of Engineering and Science, University of Agder, Agder, Norway

    Mohan L. Kolhe

  4. Dept. of Computer Sci & Engineering, R. B. S. Engineering Technical Campus, Agra, Uttar Pradesh, India

    Brajesh Kumar Singh

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Bashir, M., Goyal, V., Giri, K.J. (2023). Deep Learning Architectures Applied on Arabic-Adapted Scripts: A Review. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 522. Springer, Singapore. https://doi.org/10.1007/978-981-19-5292-0_18

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