2 research outputs found
FCN+RL: A Fully Convolutional Network followed by Refinement Layers to Offline Handwritten Signature Segmentation
Although secular, handwritten signature is one of the most reliable biometric
methods used by most countries. In the last ten years, the application of
technology for verification of handwritten signatures has evolved strongly,
including forensic aspects. Some factors, such as the complexity of the
background and the small size of the region of interest - signature pixels -
increase the difficulty of the targeting task. Other factors that make it
challenging are the various variations present in handwritten signatures such
as location, type of ink, color and type of pen, and the type of stroke. In
this work, we propose an approach to locate and extract the pixels of
handwritten signatures on identification documents, without any prior
information on the location of the signatures. The technique used is based on a
fully convolutional encoder-decoder network combined with a block of refinement
layers for the alpha channel of the predicted image. The experimental results
demonstrate that the technique outputs a clean signature with higher fidelity
in the lines than the traditional approaches and preservation of the pertinent
characteristics to the signer's spelling. To evaluate the quality of our
proposal, we use the following image similarity metrics: SSIM, SIFT, and Dice
Coefficient. The qualitative and quantitative results show a significant
improvement in comparison with the baseline system.Comment: 7 pages, 6 figures, Accepted at IJCNN 2020: International Joint
Conference on Neural Network
A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation
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.Comment: This paper was accepted for IJCNN 2020 Conferenc