Detection of Personality Traits Using Handwriting and Deep Learning
Source abstract
Abstract
A series of studies and research have shown the existence of a link between handwriting and a person’s personality traits. There are numerous fields that require a psychological assessment of individuals, where there is a need to determine personality traits in a faster and more efficient manner than that based on classic questionnaires or graphological analysis. The development of image processing and recognition algorithms based on machine learning and deep neural networks has led to a series of applications in the field of graphology. In the present study, a system for automatically extracting handwriting characteristics from written documents and correlating them with Myers–Briggs type indicator is implemented. The system has an architecture composed of three levels, the main level being formed by four convolutional neural networks. To train the networks, a database with different types of handwriting was created. The experimental results show an accuracy ranging between 89% and 96% for handwritten features’ recognition and results ranging between 83% and 91% in determining Myers–Briggs indicators.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Handwritten Text Recognition Techniques · Natural Language Processing Techniques · Image Processing and 3D Reconstruction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Automatic Control and Electronics, University of Craiova, 200585 Craiova, Romania
References (33)
Show source-provided references
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