A Fully Convolutional Network for Signature Segmentation from Document Images
Victor Kleber Santos Leite Melo, Byron Leite Dantas Bezerra · 2018
Handwritten signatures can be employed as a sign of confirmation in a wide variety of documents, namely, bank checks, identification documents and a variety of business certificates and contracts. Since those documents present complex backgrounds, the automatic extraction of handwritten signature from documents remains as an open task in the Offline Signature Verification field. In this paper we propose a method for the stroke-based extraction of signatures from document images. The approach is based on a Fully Convolutional Network trained to learn an end-to-end nonlinear mapping to extract the signatures from documents. Due to the lack of publicly available datasets containing the ground truth of signatures on the stroke level, we trained and evaluated our model on a dataset we created synthetically from real documents. It contains the stroke-based ground truth of signatures in a variety of documents with complex backgrounds. As a contribution of this work, the dataset will be made publicly available. Our method shows promising results on the test set, 89.8% recall and 66.9% precision.