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Ligature based Urdu Nastaleeq sentence recognition using gated bidirectional long short term memory

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Abstract

Bidirectional long short term memory (BLSTM) architecture—a special case of recurrent neural network—is successfully applied for recognition of Urdu Nastaleeq sentence images based on character information. In such cases, manual labeling of characters in sentences for a large dataset is an intensive job, because identical characters observe different shapes at different positions inside ligatures and words. On the other hand, labeling any dataset with ligatures is a relatively easier and more accurate phenomenon. In the current paper, we propose a novel gated BLSTM (GBLSTM) model for recognition of printed Urdu Nastaleeq text based on ligature information. Our proposed model incorporates raw pixel values as features instead of human crafted features, because of the latter being more error prone. The model is trained on un-degraded and tested on unseen artificially degraded versions of Urdu printed text images dataset. The recognition accuracy of the proposed GBLSTM model is 96.71% that is higher than the prevalent Urdu optical character recognition systems.

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Notes

  1. UPTIs dataset is provided by faisal.shafait@uwa.edu.au and adnan@cs.uni-kl.de.

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Acknowledgements

The authors would like to thank Dr. Ruifan Li, Center for Intelligence of Science and Technology (CIST), Beijing University of Posts and Telecommunications, China. We would also like to thank Mr. Mohammad Saad Khan, Beijing University of Posts and Telecommunications, China for his guidance and unprecedented help.

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Authors and Affiliations

  1. Center for Intelligence of Science and Technology (CIST), Beijing University of Posts and Telecommunications, Beijing, China

    Ibrar Ahmad, Xiaojie Wang, Yuz hao Mao & Guang Liu

  2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

    Haseeb Ahmad

  3. Nanjing University of Information Sciences and Technology, Nanjing, China

    Rahat Ullah

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  1. Ibrar Ahmad
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  2. Xiaojie Wang
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  3. Yuz hao Mao
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Correspondence to Ibrar Ahmad.

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Ahmad, I., Wang, X., Mao, Y.h. et al. Ligature based Urdu Nastaleeq sentence recognition using gated bidirectional long short term memory. Cluster Comput 21, 703–714 (2018). https://doi.org/10.1007/s10586-017-0990-5

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