  Handwritten signature verification, crucial for legal and financial
institutions, faces challenges including inter-writer similarity, intra-writer
variations, and limited signature samples. To address these, we introduce a
MultiScale Signature feature learning Network (MS-SigNet) with a novel metric
learning loss called the co-tuplet loss, designed for offline handwritten
signature verification. MS-SigNet learns both global and regional signature
features from multiple spatial scales, enhancing feature discrimination. This
approach effectively distinguishes genuine signatures from skilled forgeries by
capturing overall strokes and detailed local differences. The co-tuplet loss,
focusing on multiple positive and negative examples, overcomes the limitations
of typical metric learning losses by addressing inter-writer similarity and
intra-writer variations and emphasizing informative examples. We also present
HanSig, a large-scale Chinese signature dataset (available at
https://github.com/hsinmin/HanSig) to support robust system development.
Experimental results on four benchmark datasets in different languages
demonstrate the promising performance of our method in comparison to
state-of-the-art approaches.
