Evaluation of deep learning approaches for optical character recognition in Urdu language

  • Mehak Riaz College of Computing and Information Sciences, Karachi Institute of Economics and Technology, Karachi Pakistan
  • Syed Muhammad Ghazanfar Monir Department of Electrical and Computer Engineering, Mohammad Ali Jinnah University, Karachi Pakistan
  • Rija Hasan Department of Electrical and Computer Engineering, Mohammad Ali Jinnah University, Karachi Pakistan

Abstract

With the evolving technological era, the optical character recognition systems have substantial execution, considering the widespread use of daily hand-written human transaction. Optical Character Recognition (OCR) is an implementation of Computer Vision that digitizes numerous hand dealt documents for further analysis and formatting. OCR is achieved by various ways such as discriminative analysis and deep learning. This paper focuses on evaluating deep learning models on a hand-written compiled dataset of Urdu digits. The evaluation is performed for deep convolutional learning algorithms; VGGNet16, InceptionV3, ResNet50, and DenseNet121. The convolutional models are pre-trained on the ImageNet. The model weights of fully connected layers have been evaluated, reducing the training time of the convolutional layers. The testing accuracy for the compiled dataset is observed as, ResNet50 with 96%, InceptionV3 with 95%, VGGNet16 with 95% and DenseNet121 with 94%.

Published
Dec 29, 2022
How to Cite
RIAZ, Mehak; MONIR, Syed Muhammad Ghazanfar; HASAN, Rija. Evaluation of deep learning approaches for optical character recognition in Urdu language. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 41, n. 4, p. 146-156, dec. 2022. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2693>. Date accessed: 24 nov. 2024. doi: http://dx.doi.org/10.22581/muet1982.2204.15.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License