Detail publikace

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

ŠPAŇHEL, J. SOCHOR, J. JURÁNEK, R. HEROUT, A. MARŠÍK, L. ZEMČÍK, P.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

holistic license plate recognition, convolutional neural network, low resolution, low quality

Autoři

ŠPAŇHEL, J.; SOCHOR, J.; JURÁNEK, R.; HEROUT, A.; MARŠÍK, L.; ZEMČÍK, P.

Vydáno

3. 8. 2017

Nakladatel

IEEE Computer Society

Místo

Lecce

ISBN

978-1-5386-2939-0

Kniha

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

Strany od

1

Strany do

6

Strany počet

6

URL

http://ieeexplore.ieee.org/abstract/document/8078501/

BibTex

@inproceedings{BUT144463,
  author="Jakub {Špaňhel} and Jakub {Sochor} and Roman {Juránek} and Adam {Herout} and Lukáš {Maršík} and Pavel {Zemčík}",
  title="Holistic Recognition of Low Quality License Plates by CNN using Track Annotated Data",
  booktitle="International Workshop on Traffic and Street Surveillance for Safety and Security (AVSS 2017)",
  year="2017",
  pages="1--6",
  publisher="IEEE Computer Society",
  address="Lecce",
  doi="10.1109/AVSS.2017.8078501",
  isbn="978-1-5386-2939-0",
  url="http://ieeexplore.ieee.org/abstract/document/8078501/"
}