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Ligature categorization based Nastaliq Urdu recognition using deep neural networks

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Abstract

The cursive nature, Nastaliq writing style and a large number of different ligatures make ligature recognition very difficult in Urdu. In this paper, we present a segmentation-free approach to holistically recognize Urdu ligatures. We first generate a rich dataset which contains 17,010 ligatures with different orientation and different degrees of noise. Secondly, the ligatures are clustered (categorized) in order to reduce the search space and make the learning robust. Finally, we employ a deep neural network with dropout regularization to classify ligatures. The detailed experiments show that a deep neural network with dropout regularization and clustering of ligatures significantly enhances the classification accuracy.

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Notes

  1. www.cle.org.pk/software/ling_resources/UrduHighFreqLigature.htm.

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Authors and Affiliations

  1. Department of Computer Science COMSATS, Institute of Information Technology, Vehari, Pakistan

    Muhammad Jawad Rafeeq

  2. Department of Computer Science COMSATS, Institute of Information Technology, Abbottabad, Pakistan

    Zia ur Rehman, Ahmad Khan, Iftikhar Ahmed Khan & Waqas Jadoon

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  1. Muhammad Jawad Rafeeq
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  2. Zia ur Rehman
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  3. Ahmad Khan
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  4. Iftikhar Ahmed Khan
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  5. Waqas Jadoon
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Correspondence to Zia ur Rehman.

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Rafeeq, M.J., ur Rehman, Z., Khan, A. et al. Ligature categorization based Nastaliq Urdu recognition using deep neural networks. Comput Math Organ Theory 25, 184–195 (2019). https://doi.org/10.1007/s10588-018-9271-y

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