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
Highlights
Automatic license plate recognition (LPR) is an important research subject due to its many applications
According to Synergy Saver CEO Mr Shariffpudin Basiron; threshold value M divides the matrix into four quadrants
The objective of this paper is to propose an adaptive thresholding method for thresholding both the number plate from the car image and the individual characters within the number plate region, and to compare its results with those obtained by a fixed threshold, another state of the art thresholding method, as well as Otsu’s automatic thresholding method, in relation to a Malaysian license plate recognition system
Summary
Automatic license plate recognition (LPR) is an important research subject due to its many applications. The halo or illumination and unstandardized Malaysian plate issues have made license plate recognition continues to face big challenges [5]–[9]. We believed that a collaboration between researchers (UKM) and industry will be able to solve the issues regarding Malaysia license plate recognition. The objective of this paper is to propose an adaptive thresholding method for thresholding both the number plate from the car image and the individual characters within the number plate region, and to compare its results with those obtained by a fixed threshold, another state of the art thresholding method, as well as Otsu’s automatic thresholding method, in relation to a Malaysian license plate recognition system.
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