4,370 research outputs found
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory,
Tools and Applications (IPTA 2017
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
Automatic License Plate Recognition (ALPR) has been a frequent topic of
research due to many practical applications. However, many of the current
solutions are still not robust in real-world situations, commonly depending on
many constraints. This paper presents a robust and efficient ALPR system based
on the state-of-the-art YOLO object detector. The Convolutional Neural Networks
(CNNs) are trained and fine-tuned for each ALPR stage so that they are robust
under different conditions (e.g., variations in camera, lighting, and
background). Specially for character segmentation and recognition, we design a
two-stage approach employing simple data augmentation tricks such as inverted
License Plates (LPs) and flipped characters. The resulting ALPR approach
achieved impressive results in two datasets. First, in the SSIG dataset,
composed of 2,000 frames from 101 vehicle videos, our system achieved a
recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better
than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%,
respectively) and considerably outperforming previous results (81.80%). Second,
targeting a more realistic scenario, we introduce a larger public dataset,
called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos
and 4,500 frames captured when both camera and vehicles are moving and also
contains different types of vehicles (cars, motorcycles, buses and trucks). In
our proposed dataset, the trial versions of commercial systems achieved
recognition rates below 70%. On the other hand, our system performed better,
with recognition rate of 78.33% and 35 FPS.Comment: Accepted for presentation at the International Joint Conference on
Neural Networks (IJCNN) 201
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