Open Access

ARTICLE

Person-Dependent Handwriting Verification for Special Education Using Deep Learning

by Umut Zeki^1,*, Tolgay Karanfiller², Kamil Yurtkan¹

1 Department of Computer Engineering, Cyprus International University, Haspolat, Nicosia, Northern Cyprus, Mersin 10, Turkey
2 Computer and Instructional Technology Teacher Education, Cyprus International University, Haspolat, Nicosia, Northern Cyprus, Mersin 10, Turkey

* Corresponding Author: Umut Zeki. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 1121-1135. https://doi.org/10.32604/iasc.2023.032554

Received 22 May 2022; Accepted 06 July 2022; Issue published 29 September 2022

Abstract

Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent. However, in crowded classrooms, it is difficult for a teacher to deal with each student individually. This problem can be overcome by using supportive education applications. However, the majority of such applications are not designed for special education and therefore they are not efficient as expected. Special education students differ from their peers in terms of their development, characteristics, and educational qualifications. The handwriting skills of individuals with special needs are lower than their peers. This makes the task of Handwriting Recognition (HWR) more difficult. To overcome this problem, we propose a new personalized handwriting verification system that validates digits from the handwriting of special education students. The system uses a Convolutional Neural Network (CNN) created and trained from scratch. The data set used is obtained by collecting the handwriting of the students with the help of a tablet. A special education center is visited and the handwritten figures of the students are collected under the supervision of special education teachers. The system is designed as a person-dependent system as every student has their writing style. Overall, the system achieves promising results, reaching a recognition accuracy of about 94%. Overall, the system can verify special education students’ handwriting digits with high accuracy and is ready to integrate with a mobile application that is designed to teach digits to special education students.

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Keywords

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