SigDINO: Adapting DINOv3 with Local Structural Matching for Writer-Independent Offline Signature Verification

Offline handwritten signature verification aims to determine the authenticity of a signature from static signature images alone. Existing methods often rely on large amounts of training data to learn discriminative signature representations. However, when only limited training data are available, their performance tends to become unstable and their generalization ability often degrades in cross-language evaluation. To address these issues, we propose SigDINO, a writer-independent offline signature verification method that adapts DINOv3, a large scale self-supervised vision model with transferable visual representations, to the signature domain. Specifically, we perform self-supervised adaptation of the pretrained model using synthetic signature images, allowing it to better capture the sparse stroke distribution of offline signatures. Intermediate stroke responsive patch tokens from the adapted transformer serve as the main local representation, while cross layer residual information from deeper layers further enhances them. These representations are then compared by the local structural matching module to compute the verification distance between signature pairs. Extensive experiments on four benchmark datasets show that SigDINO achieves state-of-the-art or competitive performance, while maintaining stable verification under limited writer settings and strong generalization in cross-language evaluation.