A deep learning based approach for localization and recognition of pakistani vehicle license plates

Umair Yousaf, Ahmad Khan, Hazrat Ali, Fiaz Gul Khan, Zia Ur Rehman, Sajid Shah, Farman Ali, Sangheon Pack, Safdar Ali

Research output: Contribution to journalArticlepeer-review

Abstract

License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates.

Original languageEnglish
Article number7696
JournalSensors
Volume21
Issue number22
DOIs
Publication statusPublished - 2021 Nov 1

Keywords

  • CNN
  • Deep learning
  • License plate
  • Localization
  • RNN
  • Recognition
  • Rectification

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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