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Ligature categorization based Nastaliq Urdu recognition using deep neural networks

Author

Listed:
  • Muhammad Jawad Rafeeq

    (Institute of Information Technology)

  • Zia Rehman

    (Institute of Information Technology)

  • Ahmad Khan

    (Institute of Information Technology)

  • Iftikhar Ahmed Khan

    (Institute of Information Technology)

  • Waqas Jadoon

    (Institute of Information Technology)

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.

Suggested Citation

  • Muhammad Jawad Rafeeq & Zia Rehman & Ahmad Khan & Iftikhar Ahmed Khan & Waqas Jadoon, 2019. "Ligature categorization based Nastaliq Urdu recognition using deep neural networks," Computational and Mathematical Organization Theory, Springer, vol. 25(2), pages 184-195, June.
    • Handle: RePEc:spr:comaot:v:25:y:2019:i:2:d:10.1007_s10588-018-9271-y
    DOI: 10.1007/s10588-018-9271-y
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