Video-Based License Plate Recognition Using Single Shot Detector and Recurrent Neural Network

Dini Adni Navastara, Nuzha Musyafira, Chastine Fatichah, Safhira Maharani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Each vehicle has its own identity, in other words, the vehicle number plate. This identity often uses in parking processing, security development, and toll systems. It is necessary to develop an automated system that can be used and supported by vehicle number plates known as License Plate Recognition (LPR). This paper proposed the LPR system based on video data CCTV using the Single Shot Detector to localize the license plate, the Connected Component Labeling to do the character segmentation, and Recurrent Neural Network to recognize the characters on the license plate. This study shows our proposed method works well based on the experimental result, with an average accuracy of 94.01 % for license plate localization, 84.08% for character segmentation, and 93.53% for character recognition.

Original languageEnglish
Title of host publicationProceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages151-154
Number of pages4
ISBN (Electronic)9781665440592
DOIs
Publication statusPublished - 2021
Event13th International Conference on Information and Communication Technology and System, ICTS 2021 - Virtual, Online, Indonesia
Duration: 20 Oct 202121 Oct 2021

Publication series

NameProceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021

Conference

Conference13th International Conference on Information and Communication Technology and System, ICTS 2021
Country/TerritoryIndonesia
CityVirtual, Online
Period20/10/2121/10/21

Keywords

  • Connected Component Labelling
  • License Plate Recognition
  • Recurrent Neural Network
  • Single Shot Detector

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