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Convolutional Neural Network Based Intelligent Handwritten Document Recognition

Sagheer Abbas1, Yousef Alhwaiti2, Areej Fatima3, Muhammad A. Khan4, Muhammad Adnan Khan5,*, Taher M. Ghazal6,7, Asma Kanwal1,8, Munir Ahmad1, Nouh Sabri Elmitwally2,9
1 School of Computer Science, National College of Business administration and Economics, Lahore, 54000, Pakistan
2 College of Computer and Information Sciences, Jouf University, Sakaka, 72341, Saudi Arabia
3 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
4 Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
5 Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, Korea
6 Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
7 School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE
8 Department of Computer Science, GC University, Lahore, Pakistan
9 Department of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo University, 12613, Egypt
* Corresponding Author: Muhammad Adnan Khan. Email:

Computers, Materials & Continua 2022, 70(3), 4563-4581.

Received 22 June 2021; Accepted 23 July 2021; Issue published 11 October 2021


This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.


Convolutional neural network; segmentation; skew; cursive characters; recognition

Cite This Article

S. Abbas, Y. Alhwaiti, A. Fatima, M. A. Khan, M. Adnan Khan et al., "Convolutional neural network based intelligent handwritten document recognition," Computers, Materials & Continua, vol. 70, no.3, pp. 4563–4581, 2022.

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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