Kata Šunjić BSc, Ana Banovac MSc, Tijana Kafadar BSc, Nena Džin BSc, Marko Žeravica BSc, Petra Mikulić BSc, Ana Penava BSc, Adriana Kulišić BSc, Zlatka Hajdić BSc, Ivana Kružić PhD, Ivan Jerković PhD, Željana Bašić PhD
{"title":"t \"时间通过笔画和斜线研究手性。","authors":"Kata Šunjić BSc, Ana Banovac MSc, Tijana Kafadar BSc, Nena Džin BSc, Marko Žeravica BSc, Petra Mikulić BSc, Ana Penava BSc, Adriana Kulišić BSc, Zlatka Hajdić BSc, Ivana Kružić PhD, Ivan Jerković PhD, Željana Bašić PhD","doi":"10.1111/1556-4029.15591","DOIUrl":null,"url":null,"abstract":"<p>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 (<i>n</i> = 64), revealing statistically significant differences (<i>p</i> ≤ 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 (<i>n</i> = 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.</p>","PeriodicalId":15743,"journal":{"name":"Journal of forensic sciences","volume":"69 6","pages":"2139-2147"},"PeriodicalIF":1.5000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The “t” time: Investigating handedness through strokes and slopes\",\"authors\":\"Kata Šunjić BSc, Ana Banovac MSc, Tijana Kafadar BSc, Nena Džin BSc, Marko Žeravica BSc, Petra Mikulić BSc, Ana Penava BSc, Adriana Kulišić BSc, Zlatka Hajdić BSc, Ivana Kružić PhD, Ivan Jerković PhD, Željana Bašić PhD\",\"doi\":\"10.1111/1556-4029.15591\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 (<i>n</i> = 64), revealing statistically significant differences (<i>p</i> ≤ 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 (<i>n</i> = 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.</p>\",\"PeriodicalId\":15743,\"journal\":{\"name\":\"Journal of forensic sciences\",\"volume\":\"69 6\",\"pages\":\"2139-2147\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of forensic sciences\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/1556-4029.15591\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, LEGAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of forensic sciences","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/1556-4029.15591","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, LEGAL","Score":null,"Total":0}
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 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.
期刊介绍:
The Journal of Forensic Sciences (JFS) is the official publication of the American Academy of Forensic Sciences (AAFS). It is devoted to the publication of original investigations, observations, scholarly inquiries and reviews in various branches of the forensic sciences. These include anthropology, criminalistics, digital and multimedia sciences, engineering and applied sciences, pathology/biology, psychiatry and behavioral science, jurisprudence, odontology, questioned documents, and toxicology. Similar submissions dealing with forensic aspects of other sciences and the social sciences are also accepted, as are submissions dealing with scientifically sound emerging science disciplines. The content and/or views expressed in the JFS are not necessarily those of the AAFS, the JFS Editorial Board, the organizations with which authors are affiliated, or the publisher of JFS. All manuscript submissions are double-blind peer-reviewed.