Ligature categorization based Nastaliq Urdu recognition using deep neural networks
Volume: 25, Issue: 2, Pages: 184 - 195
Published: Apr 16, 2018
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...
Paper Details
Title
Ligature categorization based Nastaliq Urdu recognition using deep neural networks
Published Date
Apr 16, 2018
Volume
25
Issue
2
Pages
184 - 195
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