Devising an approach to personality identification based on handwritten text using a vision transformer

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.322726

Keywords:

convolutional neural network, handwriting analysis, functional model, vision transformer

Abstract

The object of this study is the approach to personality identification based on handwritten text using machine learning methods. Increasing the accuracy of personality identification and automating feature extraction could make it possible to perform more accurate analysis of handwritten text. A functional model has been built, and an experimental study of the proposed approach was conducted. The results of the study showed that the proposed approach increased the overall accuracy of identification, compared to other studies, as evidenced by the obtained accuracy values with the lowest indicator of 94.84 % for Friendliness and the highest 99.48 % for Conscientiousness. The accuracy indicator also improved compared to other models, as evidenced by the average accuracy value, which increased from 0.65 to 0.94. Such results were obtained through the use of the "Vision Transformer" method, which makes it possible to remove the need for feature extraction as a separate step, and the scale-invariant feature transformation approach made it possible to extract relevant image patches. An experimental validation was conducted using retrieval and classification approaches, which minimizes the variability of the results. The use of the Big Five model and the CVL dataset improves the accessibility of the study for comparison and reproducibility. In practice, handwriting analysis is widely used in forensics, for personnel selection, as well as in other areas of activity. The results increase the reliability of automated handwriting analysis systems in the area of personality identification, which could help graphologists and handwriting experts in their work both to assess personality traits and to determine whether a certain handwritten text belongs to a specific person

Author Biographies

Mykyta Shupyliuk, Kharkiv National University of Radio Electronics

PhD Student

Department of Electronic Computers

Vitalii Martovytskyi, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Electronic Computers

Nataliia Bolohova, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Electronic Computers

Yuri Romanenkov, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences, Vice-Rector for Scientific Work

Serhii Osiievskyi, Ivan Kozhedub Kharkiv National Air Force University

PhD, Associate Professor

Department of Mathematics and Software of ACS

Serhii Liashenko, State Biotechnological University

Doctor of Technical Sciences, Professor

Department of Mechatronics, Life Safety and Quality Management

Oleksii Nesmiian, Ivan Kozhedub Kharkiv National Air Force University

PhD, Associate Professor

Department of Mathematics and Software of ACS

Ihor Nikiforov, Ivan Kozhedub Kharkiv National Air Force University

PhD, Associate Professor

Department of Philosophy

Vladyslav Sukhoteplyi, Ivan Kozhedub Kharkiv National Air Force University

Senior Instructor

Department of Radioelectronic Systems of Control Points of Air Forces

Yevhen Lapchenkov, Military Institute of Armored Forces of National Technical University “Kharkiv Polytechnic Institute”

Senior Instructor

Department of Armored Development and Military Equipment

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Devising an approach to personality identification based on handwritten text using a vision transformer

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Published

2025-02-27

How to Cite

Shupyliuk, M., Martovytskyi, V., Bolohova, N., Romanenkov, Y., Osiievskyi, S., Liashenko, S., Nesmiian, O., Nikiforov, I., Sukhoteplyi, V., & Lapchenkov, Y. (2025). Devising an approach to personality identification based on handwritten text using a vision transformer. Eastern-European Journal of Enterprise Technologies, 1(2 (133), 53–65. https://doi.org/10.15587/1729-4061.2025.322726

Issue

Vol. 1 No. 2 (133) (2025): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2025 Mykyta Shupyliuk, Vitalii Martovytskyi, Nataliia Bolohova, Yuri Romanenkov, Serhii Osiievskyi, Serhii Liashenko, Oleksii Nesmiian, Ihor Nikiforov, Vladyslav Sukhoteplyi, Yevhen Lapchenkov

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