Year: 2025
Type: article
Abstract: Handwriting is a neuromuscular task influenced by factors such as age, gender, and handedness, with gender being a significant feature that can aid in classification. In the present study, thirty handwriting samples were analysed using a digital tablet with MovAlyzeR software. The key dynamic features, including absolute jerk, peak vertical velocity and acceleration, loop surface area, slant and pressure, were analysed using software. The results highlighted a significant difference in absolute jerk, with males showing higher values, underscoring its importance in gender classification. Although other features did not show significant gender differences, they remain important for comprehensive handwriting analysis. The study highlights the importance of dynamic handwriting features for understanding gender differences. Further studies are underway to identify more dynamic features contributing to gender classification from digitally captured handwriting.
Source: Nowa Kodyfikacja Prawa Karnego
Authors: Priya Sharma, Spriha Sharma, Om Prakash Jasuja
Institution: Chandigarh University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Gender equality
Open Access status: diamond
OpenAlex ID: W4410695052