  The Know Your Customer (KYC) and Anti Money Laundering (AML) are worldwide
practices to online customer identification based on personal identification
documents, similarity and liveness checking, and proof of address. To answer
the basic regulation question: are you whom you say you are? The customer needs
to upload valid identification documents (ID). This task imposes some
computational challenges since these documents are diverse, may present
different and complex backgrounds, some occlusion, partial rotation, poor
quality, or damage. Advanced text and document segmentation algorithms were
used to process the ID images. In this context, we investigated a method based
on U-Net to detect the document edges and text regions in ID images. Besides
the promising results on image segmentation, the U-Net based approach is
computationally expensive for a real application, since the image segmentation
is a customer device task. We propose a model optimization based on Octave
Convolutions to qualify the method to situations where storage, processing, and
time resources are limited, such as in mobile and robotic applications. We
conducted the evaluation experiments in two new datasets CDPhotoDataset and
DTDDataset, which are composed of real ID images of Brazilian documents. Our
results showed that the proposed models are efficient to document segmentation
tasks and portable.
