Abstract
Automatic car plate localization and recognition system is a system that identifies the car plate location and recognizes the characters on the car plate input images. Within the automated system, the car plate localization stage is the first stage and is the most crucial stage as the success rate of the whole system depends heavily on it. In this paper, a Malaysian car plate localization system using Region-based Convolutional Neural Network (R-CNN) is proposed. Using transfer learning on the AlexNet CNN, the localization was greatly improved achieving best precision and recall rate of 95.19% and 97.84% respectively. Besides, the proposed R-CNN was able to localize car plates in complex scenarios such as under occlusion.
Highlights
Automated car plate localization and recognition system is one component of an intelligent transportation system
Using transfer learning on the AlexNet Convolutional Neural Network (CNN), the localization was greatly improved achieving best precision and recall rate of 95.19% and 97.84% respectively
In order to perform transfer learning based on AlexNet, the last few layers of the AlexNet are replaced with the layers that are applicable for the current task
Summary
Automated car plate localization and recognition system is one component of an intelligent transportation system. A Malaysian car plate localization system using Region-based Convolutional Neural Network (R-CNN) is proposed. In [6], the authors have proposed to localize car plate from an input image by applying Hough Transform in order to detect the location of straight lines in the image and retrieve the bounding box location based on the location of the straight lines.
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