Automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis

Abstract

Handwritten document analysis is a method used in academia that examines the patterns and strokes of a person’s handwriting in order to get a deeper understanding of that person’s personality and character. In spite of the fact that there are a number of models and methods that may be used in the investigation of automated graphology, there are a few challenges that need to be solved. Among these challenges is the identification of efficient classification techniques that provide the highest possible degree of accuracy. Within the scope of this study, we propose automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis (MRA) where the data is preprocessed using histogram equalization and the spurious line segment section is attached to the genuine line segment portion in order to segment the succeeding line from the authentic picture of the document. A deep dense network is combined with self-attention MRA in order to provide a novel approach to the investigation of authentic handwritten text. Using the most recent and cutting-edge standards that are currently in use, an evaluation is performed to determine whether or not the proposed strategy is feasible. It is observed that the proposed method obtained nearly 98% accuracy with precision of 99%.