  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.
