Study for License Plate Detection

Aung, Mie Mie; Khaing, Phyu Phyu; San, Myint

doi:10.5815/ijigsp.2019.12.05

Cited by 5 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sequences of morphological operations are applied to eliminate extraneous outlines from the thresholder image [16]. Morphological opening involves the process of erosion followed by dilation [17], whereas Morphological closing combines dilation and erosion operations [18]. Dilation consists of increasing the number of pixels at the boundaries of objects in a digital image, whereas erosion is the reverse process [19].…”

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

View full text Add to dashboard Cite

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.

show abstract