Ligature categorization based Nastaliq Urdu recognition using deep neural networks
Muhammad Jawad Rafeeq (),
Zia Rehman (),
Ahmad Khan (),
Iftikhar Ahmed Khan () and
Waqas Jadoon ()
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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
Computational and Mathematical Organization Theory, 2019, vol. 25, issue 2, No 6, 184-195
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.
Keywords: Ligatures; Nastaliq; Deep neural network; Classification; Categorization (search for similar items in EconPapers)
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