Offline Signature Verification in the Banking Domain

Valentina Arrigoni; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1354-1363

In this paper we address a specific industrial application of offline signature verification in a context arising in the banking domain. In this context we need to verify signatures placed in different types of real-world documents e.g. bank cheques. Due to the huge amount of clients the problem is framed into the writer-independent configuration. Peculiar to this industrial application is that the signatures are associated with additional information i.e. the personal data associated to the bank account. We propose a deep learning architecture to solve the signature verification of our industrial application in which we take advantage of the additional textual information with a pre-training of an image embedding component driven by a text-recognition task. Moreover our model employs an attention mechanism over the image embeddings to learn more powerful features. We extensively compare our model with a state-of-the-art siamese approach with a text recognition model adopted to specifically solve this task and using a general purpose image encoder pretrained on the ImageNet dataset instead of text recognition embeddings. Our model proves to be more effective to solve the offline signature verification in two real-world large scale datasets. Ablation studies also confirm the importance of our ideas: the pre-training of the image encoders with a text recognition task and the attention mechanism.