Ligature categorization based Nastaliq Urdu recognition using deep neural networks View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-04-16

AUTHORS

Muhammad Jawad Rafeeq, Zia ur Rehman, Ahmad Khan, Iftikhar Ahmed Khan, Waqas Jadoon

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. More... »

PAGES

1-12

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10588-018-9271-y

DOI

http://dx.doi.org/10.1007/s10588-018-9271-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103385902


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