Malaysian vehicle license plate recognition using double edge detection

Bakar, Norazhar Abu; Nawawi, Mohamad Riduwan; Abdullah, Abdul Rahim; Noordin, Aminurrashid; Musa, Zainolizam; Xian, Oon Cee

doi:10.1109/iccsce.2012.6487182

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Adaptive thresholding is an important task of converting grayscale images into a binary image [4,10]. For instance, in augmented reality, input images must be segmented to locate known markers in the scene that are used to dynamically establish the position of the camera.…”

Section: Adaptive Thresholdingmentioning

confidence: 99%

Real-Time LCD Digit Recognition System

Saad

Noor

Abdullah

et al. 2017

IJEECS

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<p> In recent years, the utilization of digital instruments in industries is quickly expanding. This is because digital instruments are typically more exact than the analog instruments, and easier to be read as they are hooked up to a liquid-crystal display (LCD). However, manual data entry from LCD display is tedious and less accurate. This paper proposes a real-time LCD digit recognition system for the industrial purposes. The system is interfaced with an IP webcam to capture the video frames from the LCD display. The digital data is pre-processed into grayscale and being cropped into a selected region of interest (ROI). Adaptive thresholding and morphological operation are applied for the digit segmentation process. Data extraction and characterization are done by utilizing neural network classifier. Finally, all the information are logged out to Microsoft Excel spreadsheet. The 90% accuracy is accomplished for 50 test images of various LCD display.</p>

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Section: Adaptive Thresholdingmentioning

confidence: 99%

Real-Time LCD Digit Recognition System

Saad

Noor

Abdullah

et al. 2017

IJEECS

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“…License plate recognition (LPR) system [1]- [3] has been extensively utilized as a part of real life applications such as criminal pursuit, automatic toll collection [4] and enhancing of Automated Enforcement System (AES) performance which aims to control traffic efficiency. In terms of security, LPR has been used in traffic management to detect the owner of the car who has breached the traffic laws and to find stolen vehicles.…”

Section: Introductionmentioning

confidence: 99%

A design of license plate recognition system using convolutional neural network

Marzuki¹,

Syafeeza²,

Wong³

et al. 2019

IJECE

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This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.

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