  Removing the background in ID Card images is a real challenge for remote
verification systems because many of the re-digitalised images present
cluttered backgrounds, poor illumination conditions, distortion and occlusions.
The background in ID Card images confuses the classifiers and the text
extraction. Due to the lack of available images for research, this field
represents an open problem in computer vision today. This work proposes a
method for removing the background using semantic segmentation of ID Cards. In
the end, images captured in the wild from the real operation, using a manually
labelled dataset consisting of 45,007 images, with five types of ID Cards from
three countries (Chile, Argentina and Mexico), including typical presentation
attack scenarios, were used. This method can help to improve the following
stages in a regular identity verification or document tampering detection
system. Two Deep Learning approaches were explored, based on MobileUNet and
DenseNet10. The best results were obtained using MobileUNet, with 6.5 million
parameters. A Chilean ID Card's mean Intersection Over Union (IoU) was 0.9926
on a private test dataset of 4,988 images. The best results for the fused
multi-country dataset of ID Card images from Chile, Argentina and Mexico
reached an IoU of 0.9911. The proposed methods are lightweight enough to be
used in real-time operation on mobile devices.
