Determining adaptive thresholds for image segmentation for a license plate recognition system

Abstract

Abstract-A vehicle license plate recognition (LPR) system is useful to many applications, such as entrance admission, security, parking control, airport and cargo, traffic and speed control. This paper describe an adaptive threshold for image segmentation applied to a system for Malaysian intelligent license plate recognition (MyiLPR). Due to the different types of license plates used, the requirements of an automatic LPR system are rather different for each country. Upon receiving the input car image, this system (MyiLPR) detects and segments the license plate based on proposed adaptive threshold via image and blob histogram, and blob agglomeration, and finally, it extracts geometric character features and classifies them using neural network. The use of the proposed adaptive threshold increased the detection, segmentation and recognition rate to 99%, 94.98% and 90% correspondingly, from 95%, 78.27% and 71.08% obtained with the fixed threshold used in the originally proposed system.

A Deep Learning-based Framework for Vehicle License Plate Detection

“…Recently, deep learning-based approaches [16], particularly those employing You Only Look Once (YOLO) architecture [17], have gained attention due to their superior performance in object detection tasks [18], [19,30]. These methods leverage large-scale datasets and powerful deep neural network architectures to achieve high accuracy as well as realtime processing capabilities [20]. However, despite these advancements, there still exist certain limitations and research gaps that need to be addressed to enhance further the effectiveness and robustness of intelligent traffic monitoring systems.…”