Vehicle License Plate Character Research Articles

Reproduction of spatial–temporal distribution of traffic loads on freeway bridges via fusion of camera video and ETC data

Understanding the spatial–temporal distribution of traffic loads on bridge decks is essential for managing traffic flow and assessing bridge load effects. While computer vision can already identify on-bridge vehicle parameters through traffic surveillance cameras, obtaining cost-effective vehicle load information remains a challenge, even though it is the most important service load for bridges. In this study, we present a methodology that applies computer vision algorithms to fuzzy match video-identified data with electronic toll collection (ETC) recorded data to reproduce the spatial–temporal distribution of traffic loads on freeway bridges. The proposed method involves three critical steps. First, multi-object detection and trajectory tracking of vehicle contours, vehicle heads, vehicle tires, and vehicle license plates are implemented using YOLOv5 and DeepSort algorithms. Second, the conversion of image coordinates to real-world coordinates is automatically calibrated using pre-measured roadside reflection points. Third, vehicle license plate characters are identified by the LPRnet algorithm and the results are fused with highway ETC records to determine vehicle weight information, while the KNN clustering algorithm is used to further infer vehicle axle weight. After these three steps, the vehicle load sequence in the video surveillance area can be reproduced. Finally, the effectiveness and feasibility of the proposed method are verified in an example of a highway bridge.

A Method for the Fusion of Results from RFID and VLPR

Radio Frequency Identification (RFID) and Vehicle License Plate Recognition (VLPR) are two main methods for vehicle identification. How to fusion the recognition results of the two methods is a problem that needs to be solved in practice. The existing fusion technology usually uses trigger acquisition for precise matching, or uses one recognition result as a reference to perform matching in another result set, while these technology does not provide a clearly algorithm or has certain limitations, and is not universal. A matching algorithm is presented in this paper, which takes advantage of the similarity metric between the results from RFID and VLPR, thus eliminating the drawbacks of existing match methods. The algorithm calculates the similarity metric of the results from RFID and VLPR by the vehicle license plate character graphics similarity, then find matching results from the two recognition results in a specific time window, finally work out precise matching result or similar matching result. The presented algorithm is simple but effective, with no need to triggering RFID and VLPR simultaneously. The algorithm can output both precise matches and similar matches. Through the verification results in the real road scene, the performance in the license plate similarity calculation and fusion matching process is consistent with expectations, which shows the effectiveness of the algorithm.

Iraqi License Plate Recognition Based on Neural Network Technique

License Plate (LP) is the unique identification of a vehicle. License Plate Recognition (LPR) is considered as one of the promising aspects of applying computer seeing technology across intelligent transportation system. We used mathematical morphology and edge detection as a method for segmentation and extracting the vehicle license plate character, in Location of the vehicle plate. Initially, the color image was changed to a gray image, and by calculating the difference between the pixels and their neighbors in order to build the edge of the image. This makes the license panel appear clearly. The Sobel operator is applied to extract the edge of objects in the image. In order to obtain a smooth image, we apply the mathematical morphology of the images for dilation and erosion, and then we improve the efficiency of segmentation processing by using the images that are sent to the LP recognition stage. Multi-layer perceptron neural network will be used to recognize the license plate. Characters can be accurately extracted from the license plate due to the accurate extraction of the plate region.

Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

Recent technological advancements have been used to improve the quality of living in smart cities. At the same time, automated detection of vehicles can be utilized to reduce crime rate and improve public security. On the other hand, the automatic identification of vehicle license plate (LP) character becomes an essential process to recognize vehicles in real time scenarios, which can be achieved by the exploitation of optimal deep learning (DL) approaches. In this article, a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition (HMODL-ALPCR) technique has been presented for smart city environments. The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them. For effective LP detection process, mask regional convolutional neural network (Mask-RCNN) model is applied and the Inception with Residual Network (ResNet)-v2 as the baseline network. In addition, hybrid sunflower optimization with butterfly optimization algorithm (HSFO-BOA) is utilized for the hyperparameter tuning of the Inception-ResNetv2 model. Finally, Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs. The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.

Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions.

Development of Iraqi License Plate Recognition System based on Canny Edge Detection Method

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny Edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to recognize and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully identified and recognized multi_style Iraqi license plates using different image situations and it was evaluated based on different metrics performance, achieving an overall system performance of 91.99%. This results shows the effectiveness of the proposed method compared with other existing methods, whose average recognition rate is 86% and the average processing time of one image is 0.242s which proves the practicality of the proposed method.

A Non-learning-oriented Recognition Algorithm of Vehicle License Plate Characters

Based on the fact that characters of vehicle license plate are standardized, a recognition algorithm of Vehicle License Plate Characters based on non-learning-oriented method is proposed. This method uses some concepts such as Shape Feature Vector (SFV, for short) to describe the shape of the to be recognized characters. Each character will be evaluated with a vector and the recognition is realized by calculating the similarity of vectors. This method achieves character recognition by vector arithmetic avoiding the learning/training process often used in some algorithms, such as artificial neural network, and SVM and so on. The recognition is simplified tremendously. The feasibility of using SFV to recognize vehicle license plate characters is proven theoretically, and then, the character recognition algorithm of vehicle license plate is evaluated by simulation. The result of the simulation shows that SFV can be used as license plate character recognition. And the license plate recognition algorithm based on SFV has an accuracy rate of 97.31%. This method is a helpful attempt of character recognition based on non-learning-oriented method.

Design and Implementation of a Vehicle License Plate Characters Recognition System based on FFBN Classifier

In this paper, a coercive and dynamic Automatic Vehicle Number Plate Recognition (AVNPR) scheme is obtained, a throng supervision structure able to detect Indian license plates; it is a cardinal procedure in Intelligent Transportation Systems (ITS). The progression of detection and identification of License Plates in this proposed system is divided in four sections: Image Pre-processing, License Plate Localization, Character Segmentation and Character Recognition. A Feed Forward Back-propagation Neural network (FFBN) Classifier is engaged for this particular system for License Plate exposure and features mining of License Plate characters. The outcomes demonstrate that the proposed system can successfully distinguish and identify License Plates even in problematical surroundings. A superior entitlement of accurateness has been received for the implication of this method and it is confirmed to be 92.13% for the withdrawal of License Plate area and 90.55% identifications of License Plate characters with greater concert than conventional approaches. The planned structure has been put into practice in MATLAB.

Segmentation and Recognition of Korean Vehicle License Plate Characters Based on the Global Threshold Method and the Cross-Correlation Matching Algorithm

Segmentation and Recognition of Korean Vehicle License Plate Characters Based on the Global Threshold Method and the Cross-Correlation Matching Algorithm

Nigerian Vehicle License Plate Recognition System using Artificial Neural Network

In this paper, a development of Nigeria Vehicle License Plate Recognition (NVLPR) system using artificial neural network is done. Vehicle License Plate Recognition (VLPR) is a special form of optical character recognition (OCR) which enables computer systems to read automatically the registration number of vehicles from digital pictures for the purpose of traffic control, security, access control to restricted areas, tracking of cars, tracing of stolen cars, identification of dangerous and reckless drivers on the road. This system is divided into three major parts: vehicle license plate detection, vehicle license plate character segmentation and License Plate character recognition. In vehicle license plate detection there are many challenges such as, complex plate background, illumination in consistencies, vehicle motion, distance changes for which edge detection analysis was explored. In this work, 200 vehicle license plates were captured, some with clear characters, others with blur and dirty stains . The character feature extraction and plate recognition accuracies were determined. Results showed that plates without blur and stain were most accurately extracted and recognized while satisfactory results were also obtained for the others.

The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network

In the today scenario technological intelligence is a higher demand after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. The proposed Saudi Arabia Vehicle License plate recognition system splits into three major parts, firstly extraction of a license plate region secondly segmentation of the plate characters and lastly recognition of each character. This act is quite challenging due to the multiformity of plate formats and the nonuniform outdoor illumination conditions during image collection. In this paper recognition of the license plates is achieved by the implementation of the Learning Vector Quantization artificial neural network. Their results are based upon their completeness in the Saudi Arabia Vehicle License plate character recognition and theirs have obtained encouraging results from proposed technique.

The Application of Image Processing Technology in the Intelligent Transportation System

Image processing-based vehicle recognition is one of the important research fields in ITS. The existing methods are all based on license plate recognition and car shape recognition. Their common problem is algorithm stability. And the license plates are easy to be changed. All information about vehicles should be used to recognize them reliably. A problem to be solved is to find a method to recognize vehicles besides license plate recognition and vehicle model recognition. Vehicle license plate location and character segmentation are critical steps in the license plate recognition system, and yet there are difficult problems to be solved. Kernel density estimation and Mean Shift theory

Research on Identification Technology of Vehicle License Plate Based on Image Processing

Automatic vehicle license plate is an important part of intelligent transportation system. The success of the plate recognition will have a deep impact on the construction of intelligent transport systems. Image processing, tilt correction, character delimitation, character recognition and matching are main applications of vehicle license plate recognition, and the above process are implemented in matlab environment. Vehicle license plate location is implemented by vehicle license plate locating method based on edge detection and morphology filter in this article. The tilt correction mode based on Hough transform is used for license plate tilt correction section; character delimitation algorithm is used for character delimitation to achieve the vehicle license plate character segmentation; character recognition method based on template matching is chosen for character recognition section in this article, and successfully identify the license plate number.

Vehicle License Plate Recognition Using Morphology and Neural Network

Automatic Identification of vehicles is a very challenging area, which is in contrast to the traditional practice of monitoring the vehicles manually. Automatic license plate (LP) recognition is one of the most promising aspects of applying computer vision techniques towards intelligent transportation system. In Location of the vehicle plate, a method of vehicle license plate character segmentation and extraction based on improved edge detection and Mathematical morphology was presented. In the first place, color images were changed into grey images, secondly through calculates the difference of each pixel and neighbourhood pixels to build up images edge and it can make the license plate stand out; Sobel operator is used to extract the edge of objects in image; then the algorithm applies the dilation and erosion mathematical morphology of binary images to get the image smooth contour. The segmentation result which is sent forward to LP recognition stage will improve further processing’s efficiency . Neural Network is used to recognize the license plate character. Because of the accuracy of the plate region extraction, the character can be extracted exactly from the plate region .

Vehicle license plate character recognition based on relative entropy function criterion

Based on gradient method,the conventional learning algorithm of Wavelet Neural Network(WNN)using the Mean Square Error(MSE)criterion may affect the convergence speed and make the process fall into local minimal.The entropy function criterion is superior to the MSE criterion,possible to improve convergence rate.Thus based on relative entropy function criterion,a novel vehicle license plate character recognition method using WNN was proposed.Firstly,image preprocessing was done on character images,including image binary,image normalization and so on.Then invariant moment of character image using the new invariant moment algorithm was extracted,which was taken as the characteristic vector.Finally,the optimized wavelet neural network was used to classify and recognize the target.Computer simulation shows that this method achieves good recognition effect.

Vehicle license plate character segmentation

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.

Improvements in Real-Time Feature-Based License Plate Character Recognition

This paper presents an effective method for segmenting and recognizing license plate characters on a moving vehicle in actual outdoor environment. It is applicable to various camera conditions and installation environment and provides some effective methods to improve the rate of recognition and procedure speed. This paper is concerned not only implementations in laboratory environment but also in outdoor environment like roads, entrances of parking areas and other similar places where license recognition is needed. The digital images used can be obtained from any digital camera. The proposed vehicle license plate character recognition methods are constructed from a feature-based approach. By regulating a character, a unique structural feature for each character can be extracted. The extracted feature is insensible to changes in the outdoor environments. The proposed recognition system has two main processes: 1) to construct the feature information and 2) to segment a character image from any object image of a license plate and identify it. The relative regulations, the similarity and dissimilarity between each character, are used as recognition features.