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Updated: Mar 29, 2026

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
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Handwriting classification in a forensic intelligence context using binary logistic regression (BLR) and

Chae Rin Song1, Marie Morelato1, James Brown2

  • 1Centre for Forensic Science, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia; School of Mathematical and Physical Sciences, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia.

Forensic Science International
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

Handwriting analysis can infer a writer's cultural background using binary logistic regression (BLR) and classification & regression tree (CRT) models. BLR achieved higher accuracy, while CRT offered better usability for non-experts in this study.

Keywords:
Handwriting analysisStatistical modellingTwo-step modelWriter’s backgroundWriter’s profile

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Area of Science:

  • Forensic Science
  • Computational Linguistics
  • Pattern Recognition

Background:

  • Handwritten documents remain crucial in investigations like fraud and drug trafficking.
  • Handwriting analysis offers potential beyond source identification, including inferring writer background profiles.
  • Digital transformation has not diminished the importance of analyzing physical documents.

Purpose of the Study:

  • To compare the effectiveness of binary logistic regression (BLR) and classification & regression tree (CRT) models for inferring writer cultural background from handwriting.
  • To evaluate a two-step modeling approach for classifying writers into Australian, Korean, and Vietnamese categories.
  • To assess the practical strengths and limitations of different statistical models in handwriting analysis.

Main Methods:

  • An experimental two-step modeling approach was used.
  • Categorical handwriting features were coded from scanned handwritten texts (N=196).
  • Models were trained to classify Australian vs. non-Australian, then non-Australians into Korean and Vietnamese.

Main Results:

  • Binary logistic regression (BLR) achieved higher classification accuracy (93.4% and 97.8%) compared to classification & regression tree (CRT) (86.7% and 94.2%).
  • CRT performed better on blind tests, correctly classifying 6 out of 7 specimens, while BLR classified 3 out of 7.
  • BLR provided detailed statistical outputs (odds ratios, significance levels), while CRT offered greater accessibility for non-statistical experts.

Conclusions:

  • A two-step modeling framework can be operationalized for early-stage writer classification from handwriting.
  • Model selection in handwriting analysis should balance interpretability, robustness, and accuracy.
  • Handwriting analysis holds potential for extracting operational insights beyond traditional comparison methods, supporting intelligence-led investigations.