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

Shupyliuk,  M.; Martovytskyi, V.; Bolohova, N.; Romanenkov, Y.; Osiievskyi, S.; Liashenko, S.; Nesmiian, O.; Nikiforov, I.; Sukhoteplyi, V.; Lapchenkov, Y.

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

dc.contributor.author	Shupyliuk, M.
dc.contributor.author	Martovytskyi, V.
dc.contributor.author	Bolohova, N.
dc.contributor.author	Romanenkov, Y.
dc.contributor.author	Osiievskyi, S.
dc.contributor.author	Liashenko, S.
dc.contributor.author	Nesmiian, O.
dc.contributor.author	Nikiforov, I.
dc.contributor.author	Sukhoteplyi, V.
dc.contributor.author	Lapchenkov, Y.
dc.date.accessioned	2025-03-04T08:26:30Z
dc.date.available	2025-03-04T08:26:30Z
dc.date.issued	2025
dc.description.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
dc.identifier.citation	Devising an approach to personality identification based on handwritten text using a vision transformer / M. Shupyliuk, V. Martovytskyi, N. Bolohova, Y. Romanenkov, S. Osiievskyi, S. Liashenko, O. Nesmiian, I. Nikiforov, V. Sukhoteplyi, Y. Lapchenkov // Eastern-European Journal of Enterprise Technologies. - 2025. - 1 (2 (133), P. 53–65. - https://doi.org/10.15587/1729-4061.2025.322726
dc.identifier.doi	https://doi.org/10.15587/1729-4061.2025.322726
dc.identifier.uri	https://openarchive.nure.ua/handle/document/29983
dc.language.iso	en
dc.subject	convolutional neural network
dc.subject	handwriting analysis
dc.subject	functional model
dc.subject	vision transformer
dc.title	Devising an approach to personality identification based on handwritten text using a vision transformer
dc.type	Article
dspace.entity.type	Publication