Comparison of Different Edge Detection Techniques Used in License Plate Localization

Mukhija, Pankaj; Priyanka, P.

doi:10.1109/ccict56684.2022.00077

Cited by 1 publication

(1 citation statement)

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“…Much like the Prewitt Operator, the Sobel Operator carries out a comparable computation but offers superior accuracy compared to the Prewitt Operator [14]. The Sobel edge detection method operates as a spatial filter using two distinct 3x3 convolutional kernels, one specialized in detecting vertical edges and the other in detecting horizontal edges [15]. The vertical Sobel operator retains this information because the alphabets and numerals on a license plate have more vertical edges than other letters.…”

Section: License Plate Localizationmentioning

confidence: 99%

A Robust License Plate Detection System Using Smart Device

Bin Mohamad Azhar,

Goh,

Check Yee

et al. 2024

JOIV : Int. J. Inform. Visualization

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The license plate recognition (LPR) system is widely employed in various applications. However, most research studies have used a fixed camera rather than a moving one. This is because the location of the vehicle plate is nearly static and easily estimated, making the use of a static camera simple for locating and detecting the scanned license plate. Images obtained with a moving camera are highly complex due to frequent background changes. Additionally, a challenge with car plates in Malaysia is their non-standardized nature. Car owners are permitted to use any font type for their license plate number, rendering existing license plate recognition systems from other countries incapable of effectively detecting license plates on Malaysian car plates. A traditional LPR system typically requires a high-quality camera and a powerful computer for costly and bulky processing. Nowadays, many smartphones come equipped with powerful processors and cameras. Android smartphones include various libraries for modifying hardware configurations such as the camera. This paper presents a robust method for detecting Malaysia's license plate number using a convolutional neural network (CNN). The CNN model from the pre-training process is imported to the Android device and tested in real-time in an on-road driving environment, resulting in an average recognition rate of 89.37%. A comprehensive Character Recognition Analysis is also presented to demonstrate the accuracy of each character. However, there is still room for improvement in recognizing the character Q.

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Section: License Plate Localizationmentioning

confidence: 99%