a robust and efficient approach to license plate detection pdf

A robust and efficient approach to license plate detection pdf

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A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

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A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

The rapid development of the license plate recognition technology has made great progress for its widespread uses in intelligent transportation system ITS. This paper has proposed a novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN Backpropagation Neural Network which is adaptable in weak illumination and complicated backgrounds. Firstly, a preprocessing is first used to strengthen the contrast ratio of original car image. Secondly, the candidate regions of license plate are checked to verify the true plate, and the license plate image is located accurately by the integral projection method. Finally, a new feature extraction model is designed using three sets of features combination, training the feature vectors through BPNN to complete accurate recognition of the license plate characters. The experimental results with different license plate demonstrate effectiveness and efficiency of the proposed algorithm under various complex backgrounds.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. A Robust and Efficient Approach to License Plate Detection Abstract: This paper presents a robust and efficient method for license plate detection with the purpose of accurately localizing vehicle license plates from complex scenes in real time. A simple yet effective image downscaling method is first proposed to substantially accelerate license plate localization without sacrificing detection performance compared with that achieved using the original image. Furthermore, a novel line density filter approach is proposed to extract candidate regions, thereby significantly reducing the area to be analyzed for license plate localization.

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Due to the complexity in real world, many existing license plate detection and recognition approaches are not robust and efficient enough for practical applications, therefore ALPR still a challenging task both for engineers and researchers. The framework not only accelerates the processing speed, but also achieves a better match between the two tasks. Other contributions includes an anchor-free LP localization network based on corners using a novel MG loss is proposed and a multi-resolution input image strategy is adopted for different tasks to balance the operation speed and accuracy. Experimental results on CCPD data set show the effectiveness and efficiency of our proposed approach. The resulting best model can achieve a recognition accuracy of Automatic License Plate Recognition ALPR plays an important role in Intelligent Transportation System ITS which is widely used in traffic management, intelligent surveillance and parking management in large cities [ 20 ], hence, attracts considerable research attentions in recent two decades.

It is valuable in numerous applications, such as entrance admission, security, parking control, road traffic control, and speed control. The image is then used to detect vehicles of any type car, van, bus, truck, and bike, etc. Furthermore, this image is processed using segmentation and OCR techniques to get the vehicle registration number in form of characters. Once the required information is extracted from VLNP, this information is sent to the control center for further processing. ANPR is a challenging problem, especially when the number plates have varying sizes, the number of lines, fonts, background diversity, etc. However, only a limited work exists for Pakistan vehicles.


Abstract—This paper presents a robust and efficient method for license plate detection with the purpose of accurately localizing vehicle license plates from.


A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

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 Она не испанка? - спросил Беккер. - Нет. Думаю, англичанка.

SLPNet: Towards End-to-End Car License Plate Detection and Recognition Using Lightweight CNN

 Ничего серьезного, - ответила Сьюзан, хотя вовсе не была в этом уверена.

5 comments

  • Adam L. 02.04.2021 at 14:40

    To read the full-text of this research, you can request a copy directly from the authors. Request full-text PDF.

    Reply
  • Chloe H. 03.04.2021 at 09:23

    This Paper presents a robust and efficient method for license plate detection with the purpose of approach is proposed to extract candidate regions, thereby significantly reducing the area from the owners account and no manual process.

    Reply
  • JocelГ­n B. 04.04.2021 at 19:31

    Automated license plate recognition ALPR technology is a powerful technology enabling more efficient and effective law enforcement, security, payment collection, and research.

    Reply
  • Ruth A. 08.04.2021 at 00:02

    This paper presents a robust and efficient method for license plate detection with the purpose of accurately localizing vehicle license plates.

    Reply
  • CicerГіn P. 10.04.2021 at 00:06

    Zanlorensi, Luiz S.

    Reply

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