Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

• Main Authors: Mujtaba Husnain, Malik Muhammad Saad Missen, Shahzad Mumtaz, Muhammad Zeeshan Jhanidr, Mickaël Coustaty, Muhammad Muzzamil Luqman, Jean-Marc Ogier, Gyu Sang Choi
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Applied Sciences
• Subjects: offline Urdu handwriting; Urdu handwriting recognition; convolutional neural network
• Online Access: https://www.mdpi.com/2076-3417/9/13/2758
• ISSN: 2076-3417

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.