A New Convolutional Architecture for Vietnamese Car Plate Recognition

Nguyen, Thanh-Nga; Nguyen, DucDung

doi:10.1109/kse.2018.8573375

Cited by 14 publications

(4 citation statements)

References 7 publications

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“…CNN-L3 ( Table 3 ) is a network structure that was generated by Nguyen and Nguyen [ 56 ]. Only 1000 vehicle license plate images were trained in this model, which can be considered a “low data situation”.…”

Section: Methodsmentioning

confidence: 99%

“…Inspired by earlier work [ 54 , 55 ], Nguyen and Nguyen revised the CNN models to construct a network with three convolutional layers, three pooling layers, and two fully connected layers to solve the previous problem. They achieved a seven-digit vehicle LPR [ 56 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Captcha recognition techniques have successfully identified characters on the license plate [ 56 ]. JasonLiTW [ 57 ] aimed to solve captcha for booking tickets for the Taiwan High-Speed Rail (Simple Railway Captcha Solver (SRCS)).…”

Section: Literature Reviewmentioning

confidence: 99%

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Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Chou

Liu

2021

Sensors

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Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Chou

Liu

2021

Sensors

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“…Data processing pipeline unification has been done by [15] to fully utilise CNN to detect and recognise the LP characters, bypassing any unnecessary architectures for different tasks but showing that CNN favours detection tasks but not character recognition. Another research exploited big data (about 250k images), namely Chinese City Parking Dataset (CCPD) with one pass CNN much like SSD to recognise and localise the LP, and it is proven to be effective and robust for various environments (blurry, angled, tilted LP), avoiding recurrent CNN computation like R-CNN which is the reason of high computing cost for CNN inferencing [16].…”

Section: Related Work a Transition Of Alpr To Deep Learning Algorithmmentioning

confidence: 99%

License Plate Detection Using Convolutional Neural Network–Back to the Basic With Design of Experiments

2022

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Automatic License Plate Recognition (ALPR) is one of the applications that hugely benefited from Convolutional Neural Network (CNN) processing which has become the mainstream processing method for complex data. Many ALPR research proposed new CNN model designs and post-processing methods with various levels of performances in ALPR. However, good performing models such as YOLOv3 and SSD in more general object detection and recognition tasks could be effectively transferred to the license plate detection application with a small effort in model tuning. This paper focuses on the design of experiment (DOE) of training parameters in transferring YOLOv3 model design and optimising the training specifically for license plate detection tasks. The parameters are categorised to reduce the DOE run requirements while gaining insights on the YOLOv3 parameter interactions other than seeking optimized train settings. The result shows that the DOE effectively improve the YOLOv3 model to fit the vehicle license plate detection task.

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Deep Learning Framework for Detecting, Classifying, and Recognizing Invoice Metadata

Doan,

Nguyen,

Giang

et al. 2024

Creative Approaches Towards Development of Computing and Multidisciplinary IT Solutions for Society

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A New Convolutional Architecture for Vietnamese Car Plate Recognition

Cited by 14 publications

References 7 publications

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

License Plate Detection Using Convolutional Neural Network–Back to the Basic With Design of Experiments

Deep Learning Framework for Detecting, Classifying, and Recognizing Invoice Metadata

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