License Plate Recognition Technology Research Articles

Recognition of vehicle license plates in highway scenes with deep fusion network and connectionist temporal classification

AbstractLicense plate recognition is crucial in Intelligent Transportation Systems (ITS) for vehicle management, traffic monitoring, and security inspection. In highway scenarios, this task faces challenges such as diversity, blurriness, occlusion, and illumination variation of license plates. This article explores Recurrent Neural Networks based on Connectionist Temporal Classification (RNN‐CTC) for license plate recognition in challenging highway conditions. Four neural network models: ResNet50, ResNeXt, InceptionV3, and SENet, all combined with RNN‐CTC are comparatively evaluated. Furthermore, a novel architecture named ResNet50 Deep Fusion Network using Connectionist Temporal Classification (ResNet50‐DFN‐CTC) is proposed. Comparative and ablation experiments are conducted using the Highway License Plate Dataset of Southeast University (HLPD‐SU). Results demonstrate the superior performance of ResNet50‐DFN‐CTC in challenging highway conditions, achieving 93.158% accuracy with a processing time of 7.91 ms, outperforming other tested models. This research contributes to advancing license plate recognition technology for real‐world highway applications under adverse conditions.

An IoT vehicle management framework based on optimized license plate recognition

With the continuous development of urban traffic and the increasing number of vehicles, vehicle management has become increasingly crucial. The Harmony-based IoT vehicle management system, leveraging Internet of Things (IoT) technology, offers an effective solution. This paper introduces the concept and principles of the Harmony-based IoT vehicle management system, focusing on optimized license plate recognition. Firstly, the principles and optimization methods of license plate recognition technology are elaborately described, encompassing aspects such as image processing, feature extraction, and pattern recognition. Subsequently, the paper explores the application of advanced YOLOv7 license plate recognition technology in the Harmony-based IoT vehicle management system, incorporating discussions on interlinking with devices like gate control systems.

Development analysis and future of computer vision-based automobile license plate recognition technology and perception technology

With the increasing number of automobiles, there is a growing demand for the recognition and perception of license plates. As a unique identifier for each vehicle, license plates can help traffic management departments with vehicle tracking, real-time monitoring, and other tasks. License plate recognition and perception methods based on these technologies have been widely used. This paper aims to analyze and summarize the development of computer vision-based license plate recognition technology. The paper mainly discusses the two key steps in the license plate recognition process: license plate localization and detection, and character segmentation and recognition. These steps are classified into traditional methods and deep learning-based methods, and several methods are introduced for each category. Finally, by summarizing their advanced features and limitations, the paper compares them and predicts possible ways to improve them in the future.

Revolutionizing law enforcement: The role of artificial intelligence in license plate recognition

This paper aims to offer a comprehensive review of the current state of the art in artificial intelligence (AI) as applied to license plate recognition. With the rapidly evolving nature of AI technology, deep learning approaches have gained popularity in license plate recognition, as exemplified by the success of AlphaGo. The diversity of AI in license plate recognition is notable, with numerous studies proposing systems that have achieved high accuracy in segmentation and recognition. The process of reading license plates is complex and involves several stages, including image capture, pre-processing, license plate identification, character segmentation, and recognition. Law enforcement widely uses automatic license plate recognition (ALPR) technology for detecting and preventing criminal activities, tracking stolen vehicles, and identifying suspects. Additionally, ALPR technology can monitor travel time on significant roadways, which can provide the Department of Transportation with useful data for efficient traffic management. Overall, this paper highlights the importance of AI in license plate recognition and its potential to revolutionize the field.

Research on License Plate Recognition Algorithm in Dark-light Environment Based on Deep Learning

The advent of intelligent transportation technology has witnessed substantial advancements in license plate recognition technology, thereby assuming a crucial role in contemporary traffic monitoring. Nevertheless, conventional license plate recognition technology is inherently constrained by lighting conditions, severely impeding its nocturnal application. Given the escalating demand for license plate recognition technology in nighttime traffic surveillance, traffic violation documentation, and parking facilities, there exists a significant scholarly impetus to investigate nighttime license plate recognition technology. This study primarily focuses on nocturnal license plate recognition and employs deep learning techniques to execute comprehensive license plate recognition tasks. By leveraging this approach, the accuracy of license plate recognition in low-light environments can be substantially enhanced, thereby fortifying nocturnal traffic supervision and augmenting vehicular safety levels.

Chinese License Plate Recognition System Based on Convolutional Neural Network

License Plate Recognition (LPR) technology has been developed for many years, but for the time being, LPR technology still has problems such as not being accurate enough in positioning and too long recognition time. Especially in China, the License Plate (LP) is made up of Chinese characters, alphabets, and numbers, in which the use of Chinese characters profoundly influences the accuracy of LPR. In this paper, a Convolutional Neural Network (CNN) is employed in LPR system, and the LPR system designed in this paper includes three parts: coarse LP positioning, precise LP positioning, and LP character recognition. The LPR system designed in this paper uses the functions in OpenCV to call the laptop camera and input the obtained images into the computer to achieve interframe recognition by setting the timer. Once the image is acquired, the approximate license plate position is determined by the cascade classifier. After this, the image is processed by adaptive binarization, followed by Connected Component Analysis (CCA), linear fitting, and left-right regression to precisely locate the LP. After the exact location of the LP has been determined, it will be fed into a CNN for character recognition. Simulation tests show that the LPR system implemented in this paper has some LPR capability.

License Plate Recognition via Attention Mechanism

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: .

Research on Car License Plate Recognition Based on Improved YOLOv5m and LPRNet

The application of license plate recognition technology is becoming more and more extensive. In view of the current practical requirements for the recognition accuracy and real-time performance of license plate recognition system in complex scenes, the existing target detection methods and license plate recognition methods are studied, and a car license plate recognition method based on improved YOLOv5m and LPRNet model is proposed. On the basis of studying the YOLOv5m algorithm and the image features of the car license plate, the YOLOv5m algorithm is improved from three aspects: the K-means++ algorithm is used to improve the matching degree between the anchor frame and the detection target, the DIOU loss function is used to improve the NMS method, and the feature map with 20×20 is removed to reduce the number of detection layers. A lightweight LPRNet network is used to realize license plate character recognition without character segmentation. Combining the improved YOLOv5m algorithm with LPRNet network, a license plate recognition system based on IYOLOv5m-LPRNet model is designed. The experimental results show that the average recognition accuracy of license plates in front, tilt, night and strong light interference scenes is more than 98%; Compared with the models of YOLOv3-LPRNet, YOLOv4-LPRNet, YOLOv5s-LPRNet and YOLOv5m-LPRNet, the recognition accuracy and recall rate of this method are improved, reaching 99.49% and 98.79% respectively; The mAP of this method is also the highest, reaching 98.56%; In terms of recognition speed, this method is also faster than the other four methods, and the number of pictures processed per second is increased by 5 compared with the YOLOv5m-LPRNet model. Therefore, the improved license plate recognition method in this paper performs well in robustness and speed.

LICENSE PLATE RECOGNITION TECHNIQUES: COMPARATIVE STUDY

Vehicle License Plate Recognition (LPR) has become a crucial system for various applications such as security monitoring, parking access, law enforcement and so on. LPR is employed for the identification of vehicles using their license plate. Recently, LPR technology has evolved progressively where vast improvement had been made towards the development of the image recognition’s quality and speed, as well as its state of the art methods. Although several research studies managed to resolve most of the issues that arise in LPR systems, more studies need to be conducted to improve the performance of LPR. This paper aims to provide a comprehensive analysis and comparison of different methods used in LPR. It summarizes each of the methods in terms of their accuracy, performance, strengths and weaknesses. Based on the recognition techniques used, LPR is then characterized into two categories, namely Traditional Computer Vision and Deep Learning Techniques.

PARKEY : ticket-less parking system using license plate recognition approach

As of recent practice, the majority of current parking systems are still implementing the same traditional method of giving entry to parking visitors which is by giving out a physical ticket. This has caused several problems for mall visitors. Therefore, it is planned to propose a system that can eliminate the problems and provide further improvement to the current systems. Our proposed system named PARKEY is a Ticket-less Parking System that has been designed to provide a new method of parking entry and exiting while implementing new features in the parking system. The objective of the project is to provide a working ticket-less entry and exit system by using a virtual ticket with the help of image recognition technology. With the proposed system being developed, it will help to solve problems that are present in the current systems and give an implementation of a parking system that is up to par with the current standard of technology. Before the system was developed, interviews, observation, and requirement reuse were used as methods to elicit requirements that will provide useful insights for the development of the system. Before the system was developed, interviews, observation, and requirement reuse were used as methods to elicit requirements that will provide useful insights for the development of the system. This includes providing questions to users, making observations to current systems deployed, and studying existing systems ranging from older systems to newer, similar systems. Using the stated elicitation techniques, some findings were gathered and are beneficial to the development of the system. The use of observation to current parking systems helps provides data that will be used as a benchmark for the proposed system to identify how quick the proposed system reaction time should be to provide an on-par performance with the current systems. Interviews with several potential users were done to gather information on their experience from the current parking systems and their opinions on what can be improved and expectations of the proposed system. Other than that, studying the currently available systems gives a better understanding of the drawbacks and limitations of the current systems and provides some functionalities from those systems that can be combined and implemented to benefit the proposed system’s improvement during development. The developed proposed system applies a combined usage of a web application, license plate recognition technology, and an e-wallet based payment system to tackle current parking system problems.

RETRACTED: Research on vehicle license plate recognition technology based on deep convolutional neural networks

Convolutional Neural Networks (CNN) are used for visual type recognition. This is a first-order learning method. Types of Deep Neural Networks (DNNs) training to improve vehicle identification algorithms routinely. Symptoms are difficult to diagnose accurately. Recognition is done approximately in harsh environments. The recognition accuracy of Convolutional Neural Networks (CNN) is relatively high, which requires a large number of samples. A Vehicle Logo is the most explicit vehicle manufacturer indicator and is based on the Vehicle Logo Recognition (VMR) system. Mark recognition can still be a challenge because it is difficult to pinpoint the vehicle's identity in the image and seek stability in different imaging situations simultaneously. Vehicle Manufacturer Recognition (VMR) is proposed to eliminate the requirements for accurate identification and analysis of a Convolutional Neural Network (CNN) system. The lining is used based on information obtained from a CNN source. To train the neural network use the question expansion strategy to increase the training set of the synthetic transformation and to increase the rate at which the recall. This method can be maintained with high accuracy under light changes and noise conditions and is not adaptable to the environment. Experimental results show that data sets, which show that the algorithm is capable of strong generalizations, do not have high classification accuracy. The class's average accuracy will be higher than other methods; to show that CNN is better at recognizing vehicle identification with the results of comparative tests.

Research on Pre-Processing Methods for License Plate Recognition

The license plate recognition technology is an important part of the construction of an intelligent traffic management system. This paper mainly researches the image preprocessing, license plate location, and character segmentation in the license plate recognition system. In the preprocessing part of the image, the edge detection method based on convolutional neural network (CNN) is used for edge detection. In the design of the license plate location, this paper proposes a location method based on a combination of mathematical morphology and statistical jump points. First, the license plate area is initially located using mathematical morphology-related operations and then the location of the license plate is accurately located using statistical jump points. Finally, the plate with tilt is corrected. In the process of character segmentation, the border and delimiter are first removed, then the character vertical projection method and the character boundary are used to segment the character for actually using cases.

An End to End Recognition for License Plates Using Convolutional Neural Networks

License Plate Recognition technology, as a critical part in Intelligent Transportation Systems, enjoys a broad application. LPR is a technology to read license plates (which include the character and the color) by performing vehicle detection and character recognition on surveillance videos or images. Recently, many different algorithms and models can be applied in license plate detection and character recognition. Yet given that the accuracy of the system will be impacted by image blurring caused by motion blur, low-resolution, low-luminosity, low-contrast and noisy point, concerns have been concentrated in the accuracy uncertainty of complex application scenarios impact. To address these problems, an end-to-end fast solution for number-plate recognition is proposed in this paper. The mainstream algorithm, i.e. Single Shot MultiBox Detector, is employed to auto-detect the location of the license plate. Also, to recognize consecutive characters in the detected license plate area, an end-to-end Convolutional Neural Network classification model is proposed here. The test dataset contains over 6 thousand vehicle images collected in different types of complex scenarios, which include low-resolution, low-luminosity and motion blurring. Besides, the solution proposed in this work is able to achieve 99% accuracy on the test dataset with average computing time being 30 fps, which suggests that the system can meet the requirements of real-time analysis.

A Method of License Plate Location and Character Recognition based on CNN

At the present time, the economy continues to flourish, and private cars have become the means of choice for most people. Therefore, the license plate recognition technology has become an indispensable part of intelligent transportation, with research and application value. In recent years，the convolution neural network for image classification is an application of deep learning on image processing. This paper proposes a strategy to improve the YOLO model by studying the deep learning convolutional neural network (CNN) and related target detection methods, and combines the OpenCV and TensorFlow frameworks to achieve efficient recognition of license plate characters. The experimental results show that target detection method based on YOLO is beneficial to shorten the training process and achieve a good level of accuracy.

Design of license plate recognition system based on capsule network

With the development of automobile industry, the license plate, as the unique identifier of the vehicle, plays an important role in vehicle management. License plate recognition technology is mainly realized by image processing technology. It mainly includes image preprocessing, license plate positioning, character segmentation and other processes. In the actual process of license plate recognition, it is often impossible to accurately recognize the license plate due to the positional relationship such as angle. Therefore, this paper proposes a license plate recognition program based on the capsule network. First, through image preprocessing, license plate positioning, and character segmentation, and then use capsule neural network for training and simulation in order to achieve the object of accurate character recognition.

Optoelectronic and Environmental Factors Affecting the Accuracy of Crowd-Sourced Vehicle-Mounted License Plate Recognition

License plate recognition (LPR) technology has been used to combat vehicle-related crime in urban areas in many developed contexts. However, commercially available LPR systems are expensive and not feasible for large scale adoption in developing countries. The development of a low-cost crowd-sourced solution requires an informed approach to the selection of an appropriate camera, as well as a realistic understanding of the system’s performance under various environmental conditions. This work investigates the effect of optoelectronic and environmental factors on the ability of a vehicle-mounted LPR system to correctly identify license plates, specifically for a mass-deployment crowd-sourced scenario. A theoretical LPR camera model was developed to estimate the effect of different cameras, while the effects of motion, orientation and lighting were evaluated in a series of experimental tests. The most influential optoelectronic factors were shown to be focus, focal length and image sensor resolution. Furthermore, recognition was impaired during high-speed turn maneuvers, angling of license plates away from the camera and certain night-time conditions. The optoelectronic model proved useful for the selection of a cost-effective camera for use in an open-source LPR system. Moreover, the study of environmental factors provided valuable insight into the limitations of LPR systems in various environmental and traffic conditions.

Vehicle License Plate Recognition Based on Wavelet Transform and Vertical Edge Matching

With the improvement of our country’s economic level and quality of life, the numbers and scales of highway networks and motor vehicles are constantly expanding, which makes the current road traffic burden more and more serious. As an important means of traffic automation management, license plate recognition (LPR) technology plays an important role in traffic surveillance and control. However, the recognition rate and accuracy of the traditional license plate recognition methods still need to be improved. In the case of poor surrounding environment, it is prone to localization failure, vehicle license plate recognition errors or unrecognizable phenomena. Wavelet transform, as another landmark signal processing method after Fourier transform, has been widely used in the field of image processing. In China, the number of horizontal lines is usually larger than that of vertical lines. If the two vertical boundaries of the license plate can be detected successfully, the four angles of the license plate can be determined efficiently to complete the license plate positioning. In view of the advantages of wavelet transform technology and the characteristics of vehicle license plate, in this paper, a vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching is proposed. The edge of the license plate is detected by wavelet transform technology, and then the license plate is located by vertical edge matching technology. After the location is realized, the characters are segmented by vertical projection method and the characters are recognized by improved BP neural network algorithm. The experimental results show that the proposed vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching performs well in algorithm performance, which provides a good reference for the development of vehicle license plate recognition system.

Research on License Plate Image Segmentation and Intelligent Character Recognition

With the accumulation of people’s wealth and the improvement of purchasing power, more and more people are buying cars as a means of travel. Walking and cycling of the past have now become a car trip. License plate recognition technology is especially important in intelligent transportation systems. It has been widely used in large shopping malls or supermarket parking lots, highway toll stations, speeding violation supervision and other fields. However, the accuracy and efficiency of license plate image recognition are insufficient. To solve the above problems, we propose a license plate character recognition method based on local HOG and layered LBP feature fusion from the perspectives of image pre-processing, license location, characters’ segmentation and recognition. First, pre-processing the license image area by highlighting the license plate image; then, the license plate is positioned based on wavelet decomposition and brightness moment; next the tilted license plate image is corrected, the license plate frame is adjusted, and characterization is performed by using the improved projection method based on the fact that the projection of the character is a single peak or a double peak. Finally, the local HOG and hierarchical LBP feature fusion methods are used to identify the license characters. The results show that the license plate’s character recognition rate of the proposed method reaches 99.71%, and the time taken is small. This not only improves the character recognition rate, but also saves recognition time. The results show that the method has important practical significance in license plates’ recognition.

Research and Application of License Plate Recognition Technology Based on Deep Learning

There are many types of vehicle license plates in China, including new energy license plates, large truck license plates, government vehicle license plates, and military license plates. The existing commercial license plate recognition system only targets common license plates and does not completely cover the full range of license plates. Therefore, this paper proposes an SSD-based end-to-end license plate recognition system (LPR-SSD). The LPRSSD network architecture consists of upper and lower classification networks: the upper layer network is used for vehicle license detection and classification, and the lower layer network is used for license plate character detection and classification. In order to enhance the generalization performance of the LPR-SSD network, in addition to the real license plate image captured by the camera, this paper synthesizes 50K simulated license plates for each type of license plate according to the legal document [1]. Experiments show that LPR-SSD achieved a faster convergence speed during training. After the test set verification, the accuracy of license plate location detection and classification reaches 98.3%, and the character recognition accuracy rate reaches 99.1%.

Chinese license plate image database building methodology for license plate recognition

License plate image database is the most significant factor that supports the development of license plate recognition. We first present a general method for building a license plate image database and establish a Chinese license plate image database. There are 29,015 images included in the database, named SYSU. The main work includes: first, based on a quantitative analysis of the attributes of license plate images that affect license plate recognition, relational license plate image database models are established, which consist of function and performance dataset models; second, based on the function dataset models, we present a semiautomatic method that can extract the values of the attributes in the road monitoring image and establish the function datasets, which include type and provincial abbreviation variation images. To open the function datasets, we also propose a preservative deidentification method to balance privacy protection and attribute preservation. Third, based on the performance dataset models, we establish the performance datasets, which are digital processing images from the function image datasets, including resolution, average luminance, nonuniformity of luminance, horizontal rotation angle, vertical shear angle, horizontal parallel perspective angle, vertical parallel perspective angle, out-of-focus blur, and linear uniform motion blur variation images. The recognition performance of five commercial softwares on the SYSU license plate database indicates that the database is a valuable test-bed for the evaluation and analysis of license plate recognition technology.