Holistic Recognition of Low Quality License Plates by CNN using Track Annotated Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU126440" target="_blank" >RIV/00216305:26230/17:PU126440 - isvavai.cz</a>

Result on the web

<a href="http://ieeexplore.ieee.org/abstract/document/8078501/" target="_blank" >http://ieeexplore.ieee.org/abstract/document/8078501/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/AVSS.2017.8078501" target="_blank" >10.1109/AVSS.2017.8078501</a>

Result language

angličtina

Original language name

Holistic Recognition of Low Quality License Plates by CNN using Track Annotated Data

Original language description

This work is focused on recognition of license plates in low resolution and low quality images. We present a methodology for collection of real world (non-synthetic) dataset of low quality license plate images with ground truth transcriptions. Our approach to the license plate recognition is based on a Convolutional Neural Network which holistically processes the whole image, avoiding segmentation of the license plate characters. Evaluation results on multiple datasets show that our method significantly outperforms other free and commercial solutions to license plate recognition on the low quality data. To enable further research of low quality license plate recognition, we make the datasets publicly available.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

International Workshop on Traffic and Street Surveillance for Safety and Security (AVSS 2017)

ISBN

978-1-5386-2939-0

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

1-6

Publisher name

IEEE Computer Society

Place of publication

Lecce

Event location

Lecce

Event date

Aug 28, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000426203700043