23,229 research outputs found
Iranian cashes recognition using mobile
In economical societies of today, using cash is an inseparable aspect of
human life. People use cashes for marketing, services, entertainments, bank
operations and so on. This huge amount of contact with cash and the necessity
of knowing the monetary value of it caused one of the most challenging problems
for visually impaired people. In this paper we propose a mobile phone based
approach to identify monetary value of a picture taken from cashes using some
image processing and machine vision techniques. While the developed approach is
very fast, it can recognize the value of cash by average accuracy of about 95%
and can overcome different challenges like rotation, scaling, collision,
illumination changes, perspective, and some others.Comment: arXiv #13370
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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