SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification
Sounak Dey, Anjan Dutta, J. Ignacio Toledo, Suman K. Ghosh, Josep, Llados, Umapada Pal

TL;DR
SigNet employs a convolutional Siamese network to effectively distinguish genuine signatures from forgeries in writer-independent offline signature verification, demonstrating superior performance across multiple datasets and languages.
Contribution
This paper introduces SigNet, a novel convolutional Siamese network architecture specifically designed for writer-independent offline signature verification, outperforming existing methods on benchmark datasets.
Findings
SigNet achieves state-of-the-art accuracy on multiple signature datasets.
The network generalizes well across different languages and handwriting styles.
Experimental results confirm the effectiveness of Siamese networks in signature verification.
Abstract
Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Natural Language Processing Techniques · Image Processing and 3D Reconstruction
