A vision-based machine learning method for barrier access control using vehicle license plate authentication

Kh Tohidul Islam, Ram Gopal Raj, Syed Mohammed Shamsul Islam, Sudanthi Wijewickrema, Md Sazzad Hossain, Tayla Razmovski, Stephen O’leary

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)


Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.

Original languageEnglish
Article number3578
Number of pages18
Issue number12
Publication statusPublished - 24 Jun 2020


  • Artificial neural networks
  • Automatic license plate recognition
  • Histogram of oriented gradients
  • Intelligent vehicle access

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