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Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

Gulzar Ahmed1, Tahir Alyas2, Muhammad Waseem Iqbal3,*, Muhammad Usman Ashraf4, Ahmed Mohammed Alghamdi5, Adel A. Bahaddad6, Khalid Ali Almarhabi7

1 Department of Computer Science, University of South Asia, Lahore, 54000, Pakistan
2 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
3 Department of Software Engineering, Superior University, Lahore, 54000, Pakistan
4 Department of Computer Science, GC Women University, Sialkot, 53310, Pakistan
5 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, 21493, Saudi Arabia
6 Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
7 Department of Computer Science, College of Computing in Al-Qunfudah, Umm Al-Qura University, Makkah, 24381, Saudi Arabia

* Corresponding Author: Muhammad Waseem Iqbal. Email: email

Computers, Materials & Continua 2022, 73(2), 2967-2984.


Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based extraction methods are implemented in online systems. Moreover, our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition. To this end, we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums. Subsequently, we carried out detailed experiments for character recognition, as detailed in the results. The proposed CNN model outperformed as compared to previously published approaches.


Cite This Article

G. Ahmed, T. Alyas, M. Waseem Iqbal, M. Usman Ashraf, A. Mohammed Alghamdi et al., "Recognition of urdu handwritten alphabet using convolutional neural network (cnn)," Computers, Materials & Continua, vol. 73, no.2, pp. 2967–2984, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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