Robust Automatic Recognition of Chinese License Plates in Natural Scenes

He, Ming-Xiang; Peng, Hao

doi:10.1109/access.2020.3026181

Cited by 25 publications

(6 citation statements)

References 43 publications

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“…He et al [24] presented a robust automatic recognition system for Chinese license plates in natural scenes. The proposed method employs a combination of image preprocessing, character segmentation, and recognition algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-based Framework for Vehicle License Plate Detection

Yang,

Yang

2024

IJACSA

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In the contemporary landscape of smart transportation systems, the imperative role of intelligent traffic monitoring in bolstering efficiency, safety, and sustainability cannot be overstated. Leveraging recent strides in computer vision, machine learning, and data analytics, this study addresses the pressing need for advancements in car license plate recognition within these systems. Employing an innovative approach based on the YOLOv5 architecture in deep learning, the study focuses on refining the accuracy of license plate recognition. A bespoke dataset is meticulously curated to facilitate a comprehensive evaluation of the proposed methodology, with extensive experiments conducted and metrics such as precision, recall, and F1-score employed for assessment. The outcomes underscore the efficacy of the approach in significantly enhancing the precision and accuracy of license plate recognition using performance evaluation of the proposed method. This tailored dataset ensures a rigorous evaluation, affirming the practical viability of the proposed approach in realworld scenarios. The study not only showcases the successful application of deep learning and YOLOv5 in achieving accurate license plate detection and recognition but also contributes to the broader discourse on advancing intelligent traffic monitoring for more robust and efficient smart transportation systems.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning-based Framework for Vehicle License Plate Detection

Yang,

Yang

2024

IJACSA

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“…OKM clustering technology was used for license plate segmentation, and the CNN model was used for license plate recognition. He et al [37] proposed robust automatic recognition of Chinese License plates in natural scenes. The method mainly handled and detected severely distorted car license plates.…”

Section: Related Workmentioning

confidence: 99%

License Plate Recognition via Attention Mechanism

Wang¹,

Cao²,

Zou³

et al. 2023

Computers, Materials &Amp; Continua

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License plate recognition technology use widely in intelligent traffic management and control. Researchers have been committed to improving the speed and accuracy of license plate recognition for nearly 30 years. This paper is the first to propose combining the attention mechanism with YOLO-v5 and LPRnet to construct a new license plate recognition model (LPR-CBAM-Net). Through the attention mechanism CBAM (Convolutional Block Attention Module), the importance of different feature channels in license plate recognition can be re-calibrated to obtain proper attention to features. Force information to achieve the purpose of improving recognition speed and accuracy. Experimental results show that the model construction method is superior in speed and accuracy to traditional license plate recognition algorithms. The accuracy of the recognition model of the CBAM model is increased by two percentage points to 97.2%, and the size of the constructed model is only 1.8 M, which can meet the requirements of real-time execution of embedded low-power devices. The codes for training and evaluating LPR-CBAM-Net are available under the open-source MIT License at: https:// github.com/To2rk/LPR-CBAM-Net.

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“…The automated car system has been described in which the driver's face is recognized to prove his identity at the entrance and exit by means of obtaining a picture of the car with the driver with the detection of the car with the driver's face [4]. It was applied in the identification of the car plate by suggesting the division of the application to the first plate to be maximal stable extremely region (MSER) and to determine the plate of the vehicle [5], [6]. The plates of Chinese vehicles have been identified by proposing a method distinct with its power in which afne transformations are used in discovering the plate and  ISSN: 2502-4752 avoiding errors to identify the plate of the Chinese vehicle, deep learning is the best method for automatic identification [7].…”

Section: Introductionmentioning

confidence: 99%

Distinguishing license plate numbers using discrete wavelet transform technology based deep learning

Abdulrahman¹,

Tahir²

2023

IJEECS

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Cars that violate the red light, and to increase the huge number of cars in violation, it is necessary to discover a system for identifying car plate numbers with the intervention of a computer, computer vision and neural networks segment and detail the number plates by designing regular algorithms to identify the number of license plates in violation. In this work, interest is in identifying the Iraqi car plate in order to know the place where the vehicle papers and the letters on which the vehicle depends and to know the location of the car were completed. The technique that was carried out in this work is to build new wavelets from polynomials by mathematical methods and discover a new algorithm using the MATLAB program to identify each number in the vehicle plate with a specific color by training a convolutional neural network (CNN) after analyzing the image using the new wavelets to identify the contents of the plate and good results have been reached. The accuracy level was reached with good values of up to 95%.

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Robust Automatic Recognition of Chinese License Plates in Natural Scenes

Cited by 25 publications

References 43 publications

A Deep Learning-based Framework for Vehicle License Plate Detection

A Deep Learning-based Framework for Vehicle License Plate Detection

License Plate Recognition via Attention Mechanism

Distinguishing license plate numbers using discrete wavelet transform technology based deep learning

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