Authorial approach to the detection of selected psychological traits based on handwritten texts

• Autorzy: Woda Marek , Oliwa Grzegorz

Identyfikatory

ISBN

10.24425/ijet.2024.149524

• Języki publikacji: EN

Abstrakty

EN

The study sought to use computer techniques to detect selected psychological traits based on the nature of the writing and to evaluate the effectiveness of the resulting software. Digital image processing and deep neural networks were used. The work is complex and multidimensional in nature, and the authors wanted to demonstrate the feasibility of such a topic using image processing techniques and neural networks and machine learning. The main studies that allowed the attribution of psychological traits were based on two models known from the literature, KAMR and DA. The evaluation algorithms that were implemented allowed the evaluation of the subjects and the assignment of psychological traits to them. The DA model turned out to be more effective than the KAMR model.

Słowa kluczowe

EN

prediction; handwritten text; personality traits; machine learning; neural networks

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2024
• Tom: Vol. 70, No. 1
• Strony: 145--152
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Woda Marek

• epartment of Computer Engineering, WroclawUniversity of Technology, Poland

autor

Oliwa Grzegorz

Bibliografia

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• [12] Woda, M. and Oliwa, G., 2023, July. Analysis of Handwritten Texts to Detect Selected Psychological Characteristics of a Person. In International Conference on Dependability and Complex Systems (pp. 327-341). Cham: Springer Nature Switzerland. doi:10.1007/978-3-031-37720-4_30
• [13] Wright, T., 2012. Handwriting Recognition with Artificial Neural Networks and OpenCV. CS488-Senior Capstone. https://cs.earlham.edu/~twright09/paper.pdf, last accessed 2023/1/17
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• [15] Zuckerman, M., 2002. Zuckerman-Kuhlman personality questionnaire (ZKPQ): An alternative five-factorial model. Big five assessment, pp.377-396.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).