Siamese-transformer network for offline handwritten signature verification using few-shot

Abstract

Handwritten signature verification is a crucial task with applications spanning authentication, financial transactions, and legal documents. In scenarios where only a single reference signature is available, the challenge of accurate verification becomes pronounced due to variations in writing styles, distortions, and limited labeled data. In this paper, we propose a novel Siamese-Transformer network tailored for handwritten signature verification using few-shot learning. By synergizing Siamese neural networks and Transformer architectures, our model excels in capturing contextual relationships and discerning genuine from forged signatures. A triplet loss function facilitates discriminative feature learning. Convolution layers extract local features from an image, while the transformer component utilizes these local features to capture global dependencies within signatures. Experimental results on benchmark datasets showcase the model's superior performance in few-shot verification scenarios, marking it as a promising advancement in signature verification and few-shot learning techniques.