  Signatures present on corporate documents are often used in investigations of
relationships between persons of interest, and prior research into the task of
offline signature verification has evaluated a wide range of methods on
standard signature datasets. However, such tasks often benefit from prior human
supervision in the collection, adjustment and labelling of isolated signature
images from which all real-world context has been removed. Signatures found in
online document repositories such as the United Kingdom Companies House
regularly contain high variation in location, size, quality and degrees of
obfuscation under stamps. We propose an integrated pipeline of signature
extraction and curation, with no human assistance from the obtaining of company
documents to the clustering of individual signatures. We use a sequence of
heuristic methods, convolutional neural networks, generative adversarial
networks and convolutional Siamese networks for signature extraction,
filtering, cleaning and embedding respectively. We evaluate both the
effectiveness of the pipeline at matching obscured same-author signature pairs
and the effectiveness of the entire pipeline against a human baseline for
document signature analysis, as well as presenting uses for such a pipeline in
the field of real-world anti-money laundering investigation.
