Geometric Alignment by Deep Learning for Recognition of Challenging License Plates
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU130797" target="_blank" >RIV/00216305:26230/18:PU130797 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8569259" target="_blank" >https://ieeexplore.ieee.org/document/8569259</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ITSC.2018.8569259" target="_blank" >10.1109/ITSC.2018.8569259</a>
Alternative languages
Result language
angličtina
Original language name
Geometric Alignment by Deep Learning for Recognition of Challenging License Plates
Original language description
In this paper, we explore the problem of license plate recognition in-the-wild (in the meaning of capturing data in unconstrained conditions, taken from arbitrary viewpoints and distances). We propose a method for automatic license plate recognition in-the-wild based on a geometric alignment of license plates as a preceding step for holistic license plate recognition. The alignment is done by a Convolutional Neural Network that estimates control points for rectifying the image and the following rectification step is formulated so that the whole alignment and recognition process can be assembled into one computational graph of a contemporary neural network framework, such as Tensorflow. The experiments show that the use of the aligner helps the recognition considerably: the error rate dropped from 9.6 % to 2.1 % on real-life images of license plates. The experiments also show that the solution is fast - it is capable of real-time processing even on an embedded and low-power platform (Jetson TX2). We collected and annotated a dataset of license plates called CamCar6k, containing 6,064 images with annotated corner points and ground truth texts. We make this dataset publicly available.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
ISBN
978-1-72810-321-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
3524-3529
Publisher name
IEEE Intelligent Transportation Systems Society
Place of publication
Lahaina, Maui
Event location
Maui, Hawaii
Event date
Nov 4, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000457881303079