Identifying signatures in scanned paperdocuments : A proof-of-concept at Bolagsverket

Abstract: Bolagsverket, a Swedish government agency receives cases both in paper form via mail, document form via e-mail and also digital forms. These cases may be about registering people in a company, changing the share capital, etc. However, handling and confirming all these papers can be time consuming, and it would be beneficial for Bolagsverket if this process could be automated with as little human input as possible. This thesis investigates if it is possible to identify whether a paper contains a signature or not by using artificial intelligence (AI) and convolutional neural networks (CNN), and also if it is possible to determine how many signatures a given paper has. If these problems prove to be solvable, it could potentially lead to a great benefit for Bolagsverket. In this paper, a residual neural network (ResNet) was implemented which later was trained on sample data provided by Bolagsverket. The results demonstrate that it is possible to determine whether a paper has a signature or not with a 99% accuracy, which was tested on 1000 images where the model was trained on 8787 images. A second ResNet architecture was implemented to identify the number of signatures, and the result shows that this was possible with an accuracy score of 94.6%.