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Inicio  /  Applied Sciences  /  Vol: 9 Par: 13 (2019)  /  Artículo
ARTÍCULO
TITULO

Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

Mujtaba Husnain    
Malik Muhammad Saad Missen    
Shahzad Mumtaz    
Muhammad Zeeshan Jhanidr    
Mickaël Coustaty    
Muhammad Muzzamil Luqman    
Jean-Marc Ogier and Gyu Sang Choi    

Resumen

In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.

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