Driver license field detection using real-time deep networks

Chun Ming Tsai*, Jun Wei Hsieh, Ming Ching Chang, Yu Chen Lin

*Corresponding author for this work

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

Abstract

We present an automatic system for real-time visual detection and recognition of multiple driver’s license fields using an effective deep YOLOv3 detection network. Driver licenses are essential Photo IDs frequently checked by law enforcement and insurers. Automatic detection and recognition of multiple fields from the license can replace manual key-in and significantly improve workflow. In this paper, we developed an Intelligent Driving License Reading System (IDLRS) addressing the following challenging problems: (1) varying fields and contents from multiple types and versions of driver licenses, (2) varying capturing angles and illuminations from a mobile camera, (3) fast processing for real-world applications. To retain high detection accuracy and versatility, we propose to directly detect multiple field contents in a single shot by adopting and fine-tuning the recent YOLOv3-608 detector, which can detect 11 fields from the new Taiwan driver license with accuracy of 97.5%. Our approach does not rely on text detection or OCR and outperforms them when tested with large viewing angles. To further examine such capability, we perform evaluations in 4 large tilting view configurations (top, bottom, left, right), and achieve accuracies of 93.3%, 90.2%, 97.5%, 94.3%, respectively.

Original languageEnglish
Title of host publicationTrends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
EditorsHamido Fujita, Jun Sasaki, Philippe Fournier-Viger, Moonis Ali
PublisherSpringer Science and Business Media Deutschland GmbH
Pages603-613
Number of pages11
ISBN (Print)9783030557881
DOIs
StatePublished - 2020
Event33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020 - Kitakyushu, Japan
Duration: 22 Sep 202025 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12144 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020
CountryJapan
CityKitakyushu
Period22/09/2025/09/20

Keywords

  • Deep learning
  • Driver license
  • Field detection
  • OCR
  • YOLOv3

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