TriSig-430: A Tri-Script, Multi-Session, Multi-Device Dataset toward Real-World Offline Signature Verification

Offline signature verification (OffSV) systems in real-world use must remain reliable when a query differs from the enrolled template in script, acquisition device, or time of writing. Existing OffSV datasets are mostly single-script, single-session, and single-device, so they cannot measure robustness to these shifts. We introduce **TriSig-430**, a writer-aligned tri-script offline signature dataset designed for this setting. It contains 76,374 real handwriting images from 430 writers, including 60,286 signature images and 16,088 content-controlled digit-string images. Each writer signs in three scripts: Han, Latin, and Arabic(Uyghur). A paired subset captures the same physical handwriting sheets with both a flatbed scanner and a smartphone, enabling acquisition-domain evaluation without confounding device changes with rewriting. A longitudinal cohort of 178 writers contributes samples from two sessions separated by approximately nine months, providing complementary sparse-enrollment and dense-sampling regimes. We define train-test protocols for matched and cross-script verification, cross-device transfer, few-shot enrollment, cross-session robustness, and content transfer between signatures and digit strings. TriSig-430 is intended as a unified dataset for learning and evaluating writer representations under realistic script, temporal, content, and device shifts.