Open Access

ARTICLE

A Convolutional Neural Network Based Optical Character Recognition for Purely Handwritten Characters and Digits

Syed Atir Raza^1,2, Muhammad Shoaib Farooq¹, Uzma Farooq³, Hanen Karamti ⁴, Tahir Khurshaid^5,*, Imran Ashraf^6,*

1 Department of Computer Science, School of System and Technology, University of Management and Technology, Lahore, 54000, Pakistan
2 Department of Applied Computing Technologies, FoIT&CS, University of Central Punjab, Lahore, 54590, Pakistan
3 Department of Software Engineering, University of Management and Technology, Lahore, 54000, Pakistan
4 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
5 Department of Electrical Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea
6 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea

* Corresponding Authors: Tahir Khurshaid. Email: ; Imran Ashraf. Email:

(This article belongs to the Special Issue: Enhancing AI Applications through NLP and LLM Integration)

Computers, Materials & Continua 2025, 84(2), 3149-3173. https://doi.org/10.32604/cmc.2025.063255

Received 09 January 2025; Accepted 16 May 2025; Issue published 03 July 2025

Abstract

Urdu, a prominent subcontinental language, serves as a versatile means of communication. However, its handwritten expressions present challenges for optical character recognition (OCR). While various OCR techniques have been proposed, most of them focus on recognizing printed Urdu characters and digits. To the best of our knowledge, very little research has focused solely on Urdu pure handwriting recognition, and the results of such proposed methods are often inadequate. In this study, we introduce a novel approach to recognizing Urdu pure handwritten digits and characters using Convolutional Neural Networks (CNN). Our proposed method utilizes convolutional layers to extract important features from input images and classifies them using fully connected layers, enabling efficient and accurate detection of Urdu handwritten digits and characters. We implemented the proposed technique on a large publicly available dataset of Urdu handwritten digits and characters. The findings demonstrate that the CNN model achieves an accuracy of 98.30% and an F1 score of 88.6%, indicating its effectiveness in detecting and classifying Urdu handwritten digits and characters. These results have far-reaching implications for various applications, including document analysis, text recognition, and language understanding, which have previously been unexplored in the context of Urdu handwriting data. This work lays a solid foundation for future research and development in Urdu language detection and processing, opening up new opportunities for advancement in this field.

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Keywords

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