Title: Identification of personality traits from handwritten text documents using multi-label classification models

Authors: Salankara Mukherjee; Ishita De Ghosh

Addresses: West Bengal State University, Malikapur Berunanpukuria, Barasat, North 24 Parganas, Kolkata-700126, West Bengal, India ' Department of Computer Science, Barrackpore Rastraguru Surendranath College, West Bengal, India

Abstract: Handwriting is widely investigated to mark emotional states and personality. However, the majority of the studies are based on graphology, and do not utilise personality factor models. We use the well-known five-factor model which says that people possess five basic traits, together known as big-five. Hence the problem of personality prediction from handwriting is essentially a multi-label problem. In addition to that, the predicted values should be non-binary decimal numbers since the model says people possess the traits in various degrees. Multi-label classifiers have not been explored for personality assessment using handwriting features. The current work aims to bridge the gap. Multi-label classifiers are trained by trait scores obtained by big-five inventory as well as handwriting features. A number of classifiers including classifier chain, binary relevance and label power-set are employed in the work. Best accuracies of 95.9% with non-binary label values and 97.9% with binary label values are achieved.

Keywords: multi-label classification; personality assessment; big-five traits; handwriting features; non-binary label values.

DOI: 10.1504/IJCVR.2024.135129

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.1, pp.18 - 41

Received: 25 Jan 2022
Accepted: 13 Jun 2022
Published online: 01 Dec 2023 *