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.

中文翻译：

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用于文件签名分析以检测洗钱活动的全自动管道

公司文件上的签名通常用于调查感兴趣的人之间的关系，之前对离线签名验证任务的研究已经评估了标准签名数据集上的各种方法。然而，这些任务通常受益于事先人工监督，收集、调整和标记孤立的签名图像，其中所有的现实世界上下文都已被删除。在英国公司之家等在线文档存储库中发现的签名通常在位置、大小、质量和邮票下的混淆程度方面存在很大差异。我们提出了一个集成的签名提取和管理管道，从获取公司文件到个人签名的聚类都不需要人工协助。我们使用一系列启发式方法，分别用于特征提取、过滤、清洗和嵌入的卷积神经网络、生成对抗网络和卷积连体网络。我们评估了管道在匹配模糊的同一作者签名对方面的有效性以及整个管道针对文档签名分析的人类基线的有效性，并展示了此类管道在现实世界反洗钱调查。