Open Access

ARTICLE

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

Gulzar Ahmed¹, Tahir Alyas², Muhammad Waseem Iqbal^3,*, 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:

Computers, Materials & Continua 2022, 73(2), 2967-2984. https://doi.org/10.32604/cmc.2022.029314

Received 01 March 2022; Accepted 26 April 2022; Issue published 16 June 2022

Abstract

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.

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Keywords

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