The “t” time: Investigating handedness through strokes and slopes

This study investigated the stroke and slope characteristics in left‐handed and right‐handed handwriting. Stroke (letters t, f, đ, and H) and slope (letters t, f, l, d, and g) directions were analyzed on in‐house samples (n = 64), revealing statistically significant differences (p ≤ 0.05) between th...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of forensic sciences 2024-11, Vol.69 (6), p.2139-2147
Hauptverfasser: Šunjić, Kata, Banovac, Ana, Kafadar, Tijana, Džin, Nena, Žeravica, Marko, Mikulić, Petra, Penava, Ana, Kulišić, Adriana, Hajdić, Zlatka, Kružić, Ivana, Jerković, Ivan, Bašić, Željana
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This study investigated the stroke and slope characteristics in left‐handed and right‐handed handwriting. Stroke (letters t, f, đ, and H) and slope (letters t, f, l, d, and g) directions were analyzed on in‐house samples (n = 64), revealing statistically significant differences (p ≤ 0.05) between the groups. Right‐handers predominantly exhibited left‐to‐right strokes (98%–100%), while left‐handers showed greater variability. Although statistically significant for most letters analyzed, slope direction did not demonstrate consistent patterns. A logistic regression model was developed and validated on the same sample to classify handedness based on the averaged strokes of the letter “t.” The model was further tested on samples (n = 252) from a publicly available handwriting database. If the model classified the sample as produced by left hand, it was correct in 100% of cases. In contrast, when the model classified writing as right‐handed, it was correct in 73%–97% of cases, depending on the validation sample. The model classified writing as of left‐handed origin if more than 36% of the letters “t” had a stroke from right to left, while otherwise, writing was classified as of right‐handed origin. The developed method showed great potential for classifying the handedness of the author of disputed handwriting, thus eliminating individuals as text authors or narrowing down the pool of potential authors.
ISSN:0022-1198
1556-4029
1556-4029
DOI:10.1111/1556-4029.15591