QR code localization using deep neural networks

Tamás Grósz, Péter Bodnár, László Tóth, László G. Nyúl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Usage of computer-readable visual codes became common in our everyday life at industrial environments and private use. The reading process of visual codes consists of two steps, localization and data decoding. This paper introduces a new method for QR code localization using conventional and deep rectifier neural networks. The structure of the neural networks, regularization, and training parameters, like input vector properties, amount of overlapping at samples, and effect of different block sizes are evaluated and discussed. Results are compared to localization algorithms of the literature.

Original languageEnglish
Title of host publicationIEEE International Workshop on Machine Learning for Signal Processing, MLSP
EditorsTulay Adali, Jan Larsen, Mamadou Mboup, Eric Moreau
PublisherIEEE Computer Society
ISBN (Electronic)9781479936946
DOIs
Publication statusPublished - Nov 14 2014
Event2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014 - Reims, France
Duration: Sep 21 2014Sep 24 2014

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014
CountryFrance
CityReims
Period9/21/149/24/14

Keywords

  • Machine learning
  • Neural networks
  • Object detection
  • Pattern recognition
  • QR code

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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  • Cite this

    Grósz, T., Bodnár, P., Tóth, L., & Nyúl, L. G. (2014). QR code localization using deep neural networks. In T. Adali, J. Larsen, M. Mboup, & E. Moreau (Eds.), IEEE International Workshop on Machine Learning for Signal Processing, MLSP [6958902] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). IEEE Computer Society. https://doi.org/10.1109/MLSP.2014.6958902