A Novel Deep Learning Approach for Number Plate Detection

Kavitha, M.; Saranya, S.; Santhiya, E; Swetha, D; Dharshika, R

doi:10.1109/iccmc56507.2023.10084177

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TensorFlow, an open-source library, simplifies data collection, model training, prediction resolution, and future outcome improvement processes by integrating algorithms, machine learning, and deep learning models for large-scale machine learning and numerical computing [2]. Although TensorFlow is primarily created on the Python platform, TensorFlow models may be exported and used in any Android app.…”

Section: A Softwarementioning

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

Section: A Softwarementioning

confidence: 99%