Document Type

Article

Publication Date

2026

DOI

10.3390/app16178696

Publication Title

Applied Sciences

Volume

16

Issue

17

Pages

8696 (1-23)

Abstract

Digitally captured handwriting preserves pen trajectories and dynamic signals, but it also records hardware- and input-dependent properties that can confound forensic interpretation. This study revises a support vector machine (SVM) screening framework using 16,500 samples from 30 writers, 11 writing-content categories, and five acquisition conditions spanning three tablets and stylus or finger input. Twenty-four raw and derived time-series variables were summarized by maximum, minimum, mean, median, and standard deviation, yielding 120 features; the mode statistic was removed. Writing direction and angular velocity were recalculated with atan2-based vector formulas. Unavailable device/API channels were encoded as zero, and Z-score parameters were estimated only from training folds. Writer and content evaluations used rotating pooled-“other” categories as rejection-class proxies, whereas acquisition-condition classification remained closed-set. Every outer five-fold split contained an inner five-fold forward-selection loop; RBF-SVM hyperparameters were fixed a priori (C = 1.0, gamma = scale, balanced class weights, and random seed 42). Writer classification achieved 94.85% accuracy (descriptive 95% CI: 94.69–95.01%) and 86.44% pooled-other recall. Content classification achieved 96.64% accuracy (95% CI: 95.74–97.53%) and 98.74% pooled-other recall. Acquisition-condition classification achieved 99.99% accuracy (99.98–100.00%), with one error among 16,500 outer-test predictions. The acquisition result is interpreted primarily as evidence that channel availability and device-specific measurement scales are strongly encoded in the feature space. Because the folds were sample-level, the samples were collected contemporaneously, and pooled-other writers were represented during training, these results do not establish session-disjoint, cross-device writer, or strict open-set generalization. The proposed workflow should therefore be regarded as an experimental triage aid that supports, rather than replaces, examiner-led comparison.

Rights

© 2026 by the Authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The data that support the findings of this study are available from Wen-Chao Yang upon reasonable request."

Original Publication Citation

Tsai, L.-H., Lai, H.-J., Yang, W.-C., Jiang, J., & Chen, C.-H. (2026). Automated writer and acquisition-condition classification of digitally captured handwriting using statistical dynamic features and support vector machines. Applied Sciences, 16(17), Article 8696. https://doi.org/10.3390/app16178696

ORCID

0000-0003-2958-5666 (Jiang), 0000-0002-4860-9187 (Chen)

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