Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels,optical fiber,and blockchain technology.The Internet of Things(IoT)is a network of connected,interconnected gadgets.Privacy issues occasionally arise due to the amount of data generated.However,they have been primarily addressed by blockchain and smart contract technology.While there are still security issues with smart contracts,primarily due to the complexity of writing the code,there are still many challenges to consider when designing blockchain designs for the IoT environment.This study uses traditional blockchain technology with the“You Only Look Once”(YOLO)object detection method to accurately locate and identify license plates.While YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising,they have received limited research attention.Real-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network(RCNN)built with the tensor flow core open source libraries.This method works reasonably well for identifying any license plate.The Automatic License Plate Recognition(ALPR)approach delivered outstanding results in various datasets.First,with a recognition rate of 96.2%,our system(UFPR-ALPR)surpassed the previously used technology,consisting of 4500 frames and around 150 films.Second,a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR dataset.Third,the license plate’s characters were complicated for standard methods to identify because of the shifting lighting correctly.The proposed model,however,produced beneficial outcomes.

License Plate Recognition via Attention Mechanism

License plate recognition technology use widely in intelligent trafficmanagement and control. Researchers have been committed to improving thespeed and accuracy of license plate recognition for nearly 30 years. This paperis the first to propose combining the attention mechanism with YOLO-v5and LPRnet to construct a new license plate recognition model (LPR-CBAMNet).Through the attention mechanism CBAM(Convolutional Block AttentionModule), the importance of different feature channels in license platerecognition can be re-calibrated to obtain proper attention to features. Forceinformation to achieve the purpose of improving recognition speed andaccuracy. Experimental results show that the model construction methodis superior in speed and accuracy to traditional license plate recognitionalgorithms. The accuracy of the recognition model of the CBAM model isincreased by two percentage points to 97.2%, and the size of the constructedmodel is only 1.8 M, which can meet the requirements of real-time executionof embedded low-power devices. The codes for training and evaluating LPRCBAM-Net are available under the open-source MIT License at: https://github.com/To2rk/LPR-CBAM-Net.

Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model

The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the vehicles.The VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing images.The current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN model.The presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and recognition.Hough Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP.The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters.The HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge datasets.The experimentation outcome verified that the presented method was better under several aspects.The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.

Research and Implementation of a License Plate Recognition Algorithm Based on Hierarchical Classification

This paper proposed an improved method for license plate recognition based on hierarchical classification. First, the method of feature extraction and dimension reduction is presented by finding the optimal wavelet packet basis in the process of wavelet packet decomposition and K-L transform. Then the recognition algorithm is introduced based on feature extraction and hierarchical classification. Finally, the principles and procedures of using support vector machines, Harris corner detection algorithm and digital character classification are explained in detail. Simulation results indicate that the presented recognition algorithm performs well with higher speed and efficiency in recognition.

License Plate Recognition for Parking Control System by Mathematical Morphology

Nowadays, license plate recognition for parking systems is a critical task to provide automatic control of customers and payment. This paper introduces a new method for automatic recognition of license plates of vehicles by mathematical morphology. The proposed method can provide the license plate number of the plates in different light conditions, colors, sizes, and inclination （angles）. The algorithm can recognize the license plates of European Union vehicles quickly and correctly. The pattern learning of mathematical skeletons has high efficiency in the process. The performance of the algorithm is demonstrated well by the test in a parking control system.

Deep Learning Based License Plate Number Recognition for Smart Cities

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.

A novel license plate recognition method using HTD and VTD features

In this paper, a novel method of licence plate recognition (LPR) using the vertical traverse density (VTD) and horizontal traverse density (HTD) is presented. The neutral network algorithm using VTD and HTD features is also an innovation. In addition, a so called secondary recognition method which splits characters into different parts is developed. Experimental results show that it is a simple and fast algorithm, which meets the request of real time and nicety performances of LPR and thus has applied value in intelligence transportation system (ITS).

Adversarial Attacks on License Plate Recognition Systems

The license plate recognition system(LPRS)has been widely adopted in daily life due to its efficiency and high accuracy.Deep neural networks are commonly used in the LPRS to improve the recognition accuracy.However,researchers have found that deep neural networks have their own security problems that may lead to unexpected results.Specifically,they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images,resulting in incorrect license plate recognition.There are some classic methods to generate adversarial examples,but they cannot be adopted on LPRS directly.In this paper,we modify some classic methods to generate adversarial examples that could mislead the LPRS.We conduct extensive evaluations on the HyperLPR system and the results show that the system could be easily attacked by such adversarial examples.In addition,we show that the generated images could also attack the black-box systems;we show some examples that the Baidu LPR system also makes incorrect recognitions.We hope this paper could help improve the LPRS by realizing the existence of such adversarial attacks.

Libyan Licenses Plate Recognition Using Template Matching Method

License plate recognition (LPR) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. The work presented in this paper aims to create a computer vision system capable of taking real-time input image from a static camera and identifying the license plate from extracted image. This problem is examined in two stages: First the license plate region detection and extraction from background and plate segmentation to sub-images, and second the character recognition stage. The method used for the license plate region detection is based on the assumption that the license plate area is a high concentration of smaller details, making it a region of high intensity of edges. The Sobel filter and their vertical and horizontal projections are used to identify the plate region. The result of testing this stage was an accuracy of 67.5%. The final stage of the LPR system is optical character recognition (OCR). The method adopted for this stage is based on template matching using correlation. Testing the performance of OCR resulted in an overall recognition rate of 87.76%.

Research and Implementation for License Plate Recognition Based on Improved Projection Algorithm

This paper analyzes and dissertates the discrete wavelet transform and improved projection algorithm in four kernel stages （image preprocessing, license plate localization, character segmentation, license plate recognition, i.e.） of license plate recognition system in detail. At last, it gives some conclusions and suggestions on future research.

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.

A Design Flow for Robust License Plate Localization and Recognition in Complex Scenes

In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization;2) character segmentation;and 3) feature extraction and character recognition. The algorithm uses Mexican hat operator for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate conditions. Practical considerations like existence of another text block in an image, presence of dirt or shadow on or near license plate region, license plate with rows of characters and sensitivity to license plate dimensions have been addressed. The results are encouraging with success rate of 98.10% for license plate localization and 97.05% for character recognition.

License Plate Recognition Model Based on CNN+LSTM+CTC

With the continuous improvement of the social and economic level, the number of vehicles has exploded in the city, and traditional manual identification license plates have been unable to meet the demand. In this paper, a Convolutional neural network (CNN)-based license plate recognition system is designed. The recognition module uses the CNN+LSTM+CTC model to simplify the convolutional layer structure to adapt to the lightweight training mode. The two-way LSTM structure is used to learn from both sides of the license plate to enhance the end-to-end recognition effect. Compared with the traditional scheme, the CTC loss calculation method eliminates the need for character alignment, streamlines the steps, and improves the recognition accuracy. The experiment shows that the license plate recognition software system designed in this paper has a high recognition accuracy rate of 98.59%.

PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data

Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain extent.However,the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency,which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure.To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining,herein,we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data(PURP).First,to ensure prediction accuracy,PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition(LPR)data as effective characteristics.Subsequently,to utilize the recent data without retraining the model online,PURP uses the nonparametric method k-Nearest Neighbor(namely KNN)as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online.The experimental results show that PURP retains strong prediction efficiency as the prediction period increases.

A Practical Method of Car License Plate Character Segmentation Based on Morphology and Labeling

In this paper, a kind of practical image segmentation algorithm for segment characters from car license plate is presented, based on morphology and labeling. First by morphological operation, noise in the binary image of license plate can be greatly decreased. Then, by labeling, each connected pixel component is given a unique label. Finally, by the known data of license plate, each character is extracted correctly. The advantage of this method is that it can deal with plates with different sizes and connected characters plates, and inclined plates. The experiment results show that it is an effective way to extract characters from the license plate, and can be put into practical use.

Automatic Vehicle License Recognition Based on Video Vehicular Detection System

Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system. Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented. Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold. The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%. When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system＇s requirement even under the noisy circumstance or uneven illumination.