Research on offline signature verification has explored a large variety of
methods on multiple signature datasets, which are collected under controlled
conditions. However, these datasets may not fully reflect the characteristics
of the signatures in some practical use cases. Real-world signatures extracted
from the formal documents may contain different types of occlusions, for
example, stamps, company seals, ruling lines, and signature boxes. Moreover,
they may have very high intra-class variations, where even genuine signatures
resemble forgeries. In this paper, we address a real-world writer independent
offline signature verification problem, in which, a bank's customers'
transaction request documents that contain their occluded signatures are
compared with their clean reference signatures. Our proposed method consists of
two main components, a stamp cleaning method based on CycleGAN and signature
representation based on CNNs. We extensively evaluate different verification
setups, fine-tuning strategies, and signature representation approaches to have
a thorough analysis of the problem. Moreover, we conduct a human evaluation to
show the challenging nature of the problem. We run experiments both on our
custom dataset, as well as on the publicly available Tobacco-800 dataset. The
experimental results validate the difficulty of offline signature verification
on real-world documents. However, by employing the stamp cleaning process, we
improve the signature verification performance significantly.

本文提出了一种应用 CycleGAN 和 CNN 的方法来解决真实世界中信封被遮挡的离线签名验证问题，并通过运行实验验证了该方法的提高签名验证性能的有效性。

现实世界文档中的离线签名验证

Offline Signature Verification on Real-World Documents

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 it between
dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the
capability of our network to model forgery in different languages (scripts) and
handwriting styles. Moreover, our designed Siamese network, named SigNet,
exceeds the state-of-the-art results on most of the benchmark signature
datasets, which paves the way for further research in this direction.

使用卷积 Siamese 网络，Modi 等人提出了一种离线笔迹验证的方法，其在多语言脚本和手写风格的模拟中，能够显著优于现有数据集上的最新结果，这为进一步研究提供了奠定基础。

SigNet：用于作者无关的离线签名验证的卷积孪生网络

SigNet: Convolutional Siamese Network for Writer Independent Offline  Signature Verification

Verifying the identity of a person using handwritten signatures is
challenging in the presence of skilled forgeries, where a forger has access to
a person's signature and deliberately attempt to imitate it. In offline
(static) signature verification, the dynamic information of the signature
writing process is lost, and it is difficult to design good feature extractors
that can distinguish genuine signatures and skilled forgeries. This reflects in
a relatively poor performance, with verification errors around 7% in the best
systems in the literature. To address both the difficulty of obtaining good
features, as well as improve system performance, we propose learning the
representations from signature images, in a Writer-Independent format, using
Convolutional Neural Networks. In particular, we propose a novel formulation of
the problem that includes knowledge of skilled forgeries from a subset of users
in the feature learning process, that aims to capture visual cues that
distinguish genuine signatures and forgeries regardless of the user. Extensive
experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian
PUC-PR datasets. On GPDS-160, we obtained a large improvement in
state-of-the-art performance, achieving 1.72% Equal Error Rate, compared to
6.97% in the literature. We also verified that the features generalize beyond
the GPDS dataset, surpassing the state-of-the-art performance in the other
datasets, without requiring the representation to be fine-tuned to each
particular dataset.

使用卷积神经网络和特征学习过程，以写作人无关的方式对离线签名进行识别，从而提高系统性能和对技艺熟练的伪造的辨别能力。