Siamese neural network for signature verification

This paper explores the application of a Siamese neural network to the task of handwritten signature verification. It outlines the key stages in constructing such a system, from preprocessing images to training the model using a contrastive loss function. Particular attention is given to the generation of training pairs, the selection of network architecture, and optimization strategies. The proposed system effectively addresses the challenge of signature comparison in settings with limited data, demonstrating strong robustness to variations in handwriting. The results are analyzed, and potential directions for further development of the approach are discussed.