A novel segmentation dataset for signatures on bank checks
This dataset addresses the challenge of signature extraction in complex document backgrounds, which is incremental as it builds on existing check datasets and methods.
The authors tackled the problem of extracting signatures from bank checks by creating a novel dataset with high-resolution images and segmentation masks, providing both pixel-level and patch-level annotations for training and testing networks.
The dataset presented provides high-resolution images of real, filled out bank checks containing various complex backgrounds, and handwritten text and signatures in the respective fields, along with both pixel-level and patch-level segmentation masks for the signatures on the checks. The images of bank checks were obtained from different sources, including other publicly available check datasets, publicly available images on the internet, as well as scans and images of real checks. Using the GIMP graphics software, pixel-level segmentation masks for signatures on these checks were manually generated as binary images. An automated script was then used to generate patch-level masks. The dataset was created to train and test networks for extracting signatures from bank checks and other similar documents with very complex backgrounds.