Ligature categorization based Nastaliq Urdu recognition using deep neural networks

Muhammad Jawad Rafeeq (), Zia Rehman (), Ahmad Khan (), Iftikhar Ahmed Khan () and Waqas Jadoon ()
Additional contact information
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: 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)
Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10588-018-9271-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comaot:v:25:y:2019:i:2:d:10.1007_s10588-018-9271-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10588

DOI: 10.1007/s10588-018-9271-y

Access Statistics for this article

Computational and Mathematical Organization Theory is currently edited by Terrill Frantz and Kathleen Carley

More articles in Computational and Mathematical Organization Theory from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().