A Lightweight, High-Performance Multi-Angle License Plate Recognition Model

Cheng Hung Lin, Chen Hao Wu

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

7 Citations (Scopus)


On the streets of Taiwan, many roadside tollers are often seen riding motorcycles in one hand, and the other hand holding mobile devices to issue payment notices for cars and motorcycles parked on the roadsides. The work of roadside tollers is very dangerous. First, they must first park their motorcycles next to the roadside cars and motorcycles. They then use their eyes to confirm the license plate number, enter the license plate number into the mobile device, and finally place the bill on the car's windows or attach the bill to the motorcycles. Our idea is to implement an automated license plate recognition system in mobile devices to increase the efficiency of roadside tollers and reduce their time on the road. Recently, license plate recognition systems have been widely used in various aspects of life, such as parking lot toll systems, access management systems, and traffic management systems. However, existing license plate recognition systems must have good recognition rates under a number of constraints, such as fixed angles and fixed light sources. Moreover, due to the insufficient computing resources of the general mobile device, the application cannot have a good recognition rate in the complex environment or skewed angle in the license plate recognition. Therefore, this paper proposes a lightweight and high-performance multi-Angle license plate character recognition model, which reduces the complexity and computational complexity of traditional license plate recognition. This paper also collects a large number of license plate images from different environments, angles and sizes as training data. Finally, we propose an optimized deep learning model to identify the characters on license plates. The experimental results show that the proposed model can recognize the license plate with a tilt of 060 degrees, and the overall recall rate is 84.5%. Compared with Tiny-YOLOv2, the computation of the proposed model is reduced by 61% with a little penalty of recall.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781728134802
Publication statusPublished - 2019 Aug
Event2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019 - Kusatsu, Shiga, Japan
Duration: 2019 Aug 262019 Aug 28

Publication series

NameInternational Conference on Advanced Mechatronic Systems, ICAMechS
ISSN (Print)2325-0682
ISSN (Electronic)2325-0690


Conference2019 International Conference on Advanced Mechatronic Systems, ICAMechS 2019
CityKusatsu, Shiga


  • deep learning model
  • license plate recognition system
  • lightweight
  • multi-Angle

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Mechanical Engineering


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