Classifying handwriting samples according to their type using discriminant analysis
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RIS BIB ENDNOTEClassifying handwriting samples according to their type using discriminant analysis
Publication date: 08.05.2023
Problems of Forensic Sciences, 2022, 132, pp. 241-257
https://doi.org/10.4467/12307483PFS.22.013.17686Authors
Classifying handwriting samples according to their type using discriminant analysis
Classifying handwriting samples according to their type (i.e. natural, disguised, traced, simulated or unintentionally unnatural) is an important task in handwriting analysis. It may facilitate the collection of writing standards and also help experts to assess the differences between questioned material and comparative samples or to choose the best writing features and the most relevant examination protocol for the case. Current research aimed to create a method for classifying the type of a handwriting sample using discriminant analysis. Five basic types (i.e. natural, disguised, traced, simulated and unintentionally unnatural) and some subtypes were included in this study. Participants (N = 139) wrote their full signatures, fictional signatures or a short sentence. Motor and dimensional features were assessed. The methods proved to be more than twice as accurate in classifying samples according to their type than a random choice probability (e.g. 44% as opposed to 17% for the 6-types classifier). This proof-of-a-concept study demonstrates that handwriting samples may be classified according to their type with satisfying accuracy based on their writing features and statistical tools of discriminant analysis. However, further studies are necessary to improve the accuracy of the method.
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Information: Problems of Forensic Sciences, 2022, 132, pp. 241-257
Article type: Original article
Titles:
Classifying handwriting samples according to their type using discriminant analysis
Adam Mickiewicz University in Poznań, Wieniawskiego 1, 61-712 Poznań, Poland
Published at: 08.05.2023
Received at: 27.12.2022
Accepted at: 31.01.2023
Article status: Open
Licence: CC BY-NC-ND 4.0
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English, PolishView count: 936
Number of downloads: 641
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