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Vehicle License Plate Character Segmentation 被引量:6
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作者 Mei-Sen Pan Jun-Biao Yan Zheng-Hong Xiao 《International Journal of Automation and computing》 EI 2008年第4期425-432,共8页
Vehicle license plate (VLP) character segmentation is an important part of the vehicle license plate recognition system (VLPRS).This paper proposes a least square method (LSM) to treat horizontal tilt and vertical til... Vehicle license plate (VLP) character segmentation is an important part of the vehicle license plate recognition system (VLPRS).This paper proposes a least square method (LSM) to treat horizontal tilt and vertical tilt in VLP images.Auxiliary lines are added into the image (or the tilt-corrected image) to make the separated parts of each Chinese character to be an interconnected region.The noise regions will be eliminated after two fusing images are merged according to the minimum principle of gray values. Then,the characters are segmented by projection method (PM) and the final character images are obtained.The experimental results show that this method features fast processing and good performance in segmentation. 展开更多
关键词 vehicle license plate (VLP) least square method (LSM) auxiliary lines character segmentation pixel.
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Study on the vehicle license plate tilt correction
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作者 Li Guihui Li Yuanjin Li Lanyou 《仪器仪表学报》 EI CAS CSCD 北大核心 2006年第z1期715-717,共3页
The shortage of current different approaches of the vehicle license plate(VLP) tilt correction is analyzed in the paper and a new rotary correction method put forward based on the former ways of the VLP tilt correctio... The shortage of current different approaches of the vehicle license plate(VLP) tilt correction is analyzed in the paper and a new rotary correction method put forward based on the former ways of the VLP tilt correction in the horizontal direction and the vertical direction Owing to the VLP tilt taking place in the vertical direction,the array of the image’s pixels of the same column is broken,and even different rows come into being superposition.The VLP tilt taking place in the horizontal direction,by which the array of the image’s pixels of the same row broken,and so much as different columns come into being superposition. 展开更多
关键词 tilt correction vehicle license plate image processing hough transform bilinear interpolation
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Deep Learning Based License Plate Number Recognition for Smart Cities 被引量:1
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作者 T.Vetriselvi E.Laxmi Lydia +4 位作者 Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour 《Computers, Materials & Continua》 SCIE EI 2022年第1期2049-2064,共16页
Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enha... Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods. 展开更多
关键词 Deep learning smart city tesseract computer vision vehicle license plate recognition
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Multi-Object Detection of Chinese License Plate in Complex Scenes
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作者 Dan Liu Yajuan Wu +2 位作者 Yuxin He Lu Qin Bochuan Zheng 《Computer Systems Science & Engineering》 SCIE EI 2021年第1期145-156,共12页
Multi-license plate detection in complex scenes is still a challenging task because of multiple vehicle license plates with different sizes and classes in the images having complex background.The edge features of high... Multi-license plate detection in complex scenes is still a challenging task because of multiple vehicle license plates with different sizes and classes in the images having complex background.The edge features of high-density distribution and the high curvature features of stroke turning of Chinese character are important signs to distinguish Chinese license plate from other objects.To accurately detect multiple vehicle license plates with different sizes and classes in complex scenes,a multi-object detection of Chinese license plate method based on improved YOLOv3 network was proposed in this research.The improvements include replacing the residual block of the YOLOv3 backbone network with the Inception-ResNet-A block,imbedding the SPP block into the detection network,cutting the redundant Inception-ResNet-A block to suit for the multi-license plate detection task,and clustering the ground truth boxes of license plates to obtain a new set of anchor boxes.A Chinese vehicle license plate image dataset was built for training and testing the improved network,and the location and class of the license plates in each image were accurately labeled.The dataset has 62,153 pieces of images and 4 classes of China vehicle license plates,almost images have multiple license plates with different sizes.Experiments demonstrated that the multilicense plate detection method obtained 83.4%mAP,98.88%precision,98.17%recall,98.52 F1 score,89.196 BFLOPS and 22 FPS on the test dataset,and whole performance was better than the other five compared networks including YOLOv3,SSD,Faster-RCNN,EfficientDet and RetinaNet. 展开更多
关键词 Chinese vehicle license plate multiple license plate multi-object detection Inception-ResNet-A spatial pyramid pooling
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