Automated Writer and Acquisition-Condition Classification of Digitally Captured Handwriting Using Statistical Dynamic Features and Support Vector Machines

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.