A Robust Segmentation Free License Plate Recognition Method

Khan, Arsalan; Awan, Sheryar Mehmood; Arif, Muhammad; Mahmood, Zahid; Khan, Gul Zameen

doi:10.1109/icecce47252.2019.8940769

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Vehicle LPR is widely employed in transportation [ 24 , 25 , 26 ] and comprises three steps: vehicle license plate localization, character segmentation, and character recognition [ 27 , 28 , 29 ]. However, several factors that are related to the images of license plates directly influence the difficulty of object detection and character recognition [ 30 , 31 ]. The images of vehicle license plates used in most studies are clear and non-oblique, which means the license plates directly face the camera [ 32 , 33 ], facilitating annotation and model training.…”

Section: Literature Reviewmentioning

confidence: 99%

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: Literature Reviewmentioning

confidence: 99%

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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Towards Automatic License Plate Recognition in Challenging Conditions

Sultan¹,

Khan

Shah

et al. 2023

Applied Sciences

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License plate recognition (LPR) is an integral part of the current intelligent systems that are developed to locate and identify various objects. Unfortunately, the LPR is a challenging task due to various factors, such as the numerous shapes and designs of the LPs, the non-following of standard LP templates, irregular outlines, angle variations, and occlusion. These factors drastically influence the LP appearance and significantly challenge the detection and recognition abilities of state-of-the-art detection and recognition algorithms. However, recent rising trends in the development of machine learning algorithms have yielded encouraging solutions. This paper presents a novel LPR method to address the aforesaid issues. The proposed method is composed of three distinct but interconnected steps. First, a vehicle that appears in an input image is detected using the Faster RCNN. Next, the LP area is located within the detected vehicle by using morphological operations. Finally, license plate recognition is accomplished using the deep learning network. Detailed simulations performed on the PKU, AOLP, and CCPD databases indicate that our developed approach produces mean license plate recognition accuracy of 99%, 96.0231%, and 98.7000% on the aforesaid databases.

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A Robust Segmentation Free License Plate Recognition Method

Cited by 2 publications

References 29 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

Towards Automatic License Plate Recognition in Challenging Conditions

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