A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication.

TLDR: 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.

Abstract: 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