Journal articles: 'Car license plate detection and recognition'

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Bralić, Niko, and Josip Musić. "System for automatic detection and classification of cars in traffic." St open 3 (October 31, 2022): 1–31. http://dx.doi.org/10.48188/so.3.10.

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Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library and Tesseract-based optical character recognition. For the sake of simplicity and speed, the subsystems were run on an embedded Raspberry Pi computer.Results: A camera was mounted on the inside of the windshield to monitor cars in front of the camera. The system processed the camera’s video feed and provided information on the color, license plate, make, and model of the observed car. Knowing the license plate number provides access to details about the car owner, roadworthiness, car or license place reports missing, as well as whether the license plate matches the car. Car details were saved to file and displayed on the screen. The system was tested on real-time images and videos. The accuracies of car detection and car model classification (using 8 classes) in images were 88.5% and 78.5%, respectively. The accuracies of color detection and full license plate recognition were 71.5% and 51.5%, respectively. The system operated at 1 frame per second (1 fps).Conclusion: These results show that running standard machine learning algorithms on low-cost hardware may enable the automatic detection and classification of cars in traffic. However, there is significant room for improvement, primarily in license plate recognition. Accordingly, potential improvements in the future development of the system are proposed.

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Mathew, Jess, and Chang Lee. "Vertical Edge Detection for Car License Plate Recognition." DJ Journal of Advances in Electronics and Communication Engineering 1, no. 1 (August 9, 2015): 8–15. http://dx.doi.org/10.18831/djece.org/2015011002.

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Alaidi, Abdul Hadi M., Saif Ali Abd Alradha Alsaidi, and Omar Hashim Yahya. "Plate Detection and Recognition of Iraqi License Plate Using KNN Algorithm." Journal of Education College Wasit University 1, no. 26 (January 12, 2017): 449–60. http://dx.doi.org/10.31185/eduj.vol1.iss26.102.

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This paper presents an automated car license plate recognition system applied for Iraqi vehicle plate number that developed and applied to be used in control and law enforcement related applications. In this work, the proposed license plate recognition consists of three basic stages (preprocessing, license plate localization, license plate recognition). The license plate images are pre-processed through convert image to grayscale and apply morphological transformation filter not convert the result to binary image. Then, blurs the binary image using Gaussian filter and find all contour in image using OpenCV library. In the license plate localization KNN (k-Nearest Neighbors) algorithm are used to find all possible characters in the image. The last step is done by Crop the part of image with highest candidate license plate and apply the preprocessing and license plate localization again to find and recognize all part of license plate in the cropped image.

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Hashmi, Saquib Nadeem, Kaushtubh Kumar, Siddhant Khandelwal, Dravit Lochan, and Sangeeta Mittal. "Real Time License Plate Recognition from Video Streams using Deep Learning." International Journal of Information Retrieval Research 9, no. 1 (January 2019): 65–87. http://dx.doi.org/10.4018/ijirr.2019010105.

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With ever increasing number of vehicles, vehicular management is one of the major challenges faced by urban areas. Automation in terms of detecting vehicle license plate using real time automatic license plate recognition (RT-ALPR) approach can have many use cases in automated defaulter detection, car parking and toll management. It is a computationally complex task that has been addressed in this work using a deep learning approach. As compared to previous approaches, license plates have been recognized from full camera stills as well as parking videos with noise. On a dataset of 4800 car images, the accuracy obtained is 91% on number plate extraction from images, 93% on character recognition. Proposed ALPR system has also been applied to vehicle videos shot at parking exits. Overall 85% accuracy was obtained in real-time license number recognition from these videos.

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Farag, Mohamed Sayed, Mostafa Mohamed Mohie El Din, and Hassan Ahmed Elshenbary. "Parking entrance control using license plate detection and recognition." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (July 1, 2019): 476. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp476-483.

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<p>There is no doubt that car parking is a very challenging and interesting topic of surveillance. In the recent years, a lot of smart systems for parking lot access control were developed to control and register the car data. The aim of this paper is to use image processing methods to control the entrance of a smart parking. The steps of car plate recognition are: preprocessing, License plate detection, character extraction and recognition. In the step of preprocessing, image was enhanced and noise was reduced. After preprocessing stage, color filter was used to detect the plate region. In case of large image size DWT was used for feature extraction and decreased the time of the detection stage. In the stage of character segmentation, the image is converted from grayscale to binary according to a given threshold. Filtering the binary image after using the morphological operation method, the largest objects are determined as the segmented plate characters. Finally, the correlation method was used to recognize the segmented characters. In case of similarity, SVM was used as a good classifier. Experimental results using matlab software, view that the proposed method increase the plate detection and recognition rates. It achieved aver- age 97.8% detection rate, 98% segmentation rate and 97% recognition rate, So it will be a good method for smart parking entrance control.</p>

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Aldabbagh, Ali H. A., Laith A. H. Al-Shimaysawee, and Hussein M. H. Al-Rikabi. "Novel Algorithm for Iraqi Car License Plate Detection and Recognition." Journal of Engineering and Applied Sciences 14, no. 1 (December 10, 2019): 205–10. http://dx.doi.org/10.36478/jeasci.2019.205.210.

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Aggarwal, Akarsh, Anuj Rani, and Manoj Kumar. "A robust method to authenticate car license plates using segmentation and ROI based approach." Smart and Sustainable Built Environment 9, no. 4 (October 4, 2019): 737–47. http://dx.doi.org/10.1108/sasbe-07-2019-0083.

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Purpose The purpose of this paper is to explore the challenges faced by the automatic recognition systems over the conventional systems by implementing a novel approach for detecting and recognizing the vehicle license plates in order to increase the security of the vehicles. This will also increase the societal discipline among vehicle users. Design/methodology/approach From a methodological point of view, the proposed system works in three phases which includes the pre-processing of the input image from the database, applying segmentation to the processed image, and finally extracting and recognizing the image of the license plate. Findings The proposed paper provides an analysis that demonstrates the correctness of the algorithm to correctly capture the license plate using performance metrics such as detection rate and false positive rate. The obtained results demonstrate that the proposed algorithm detects vehicle license plates and provides detection rate of 93.34 percent with false positive rate of 6.65 percent. Research limitations/implications The proposed license plate detection system eliminates the need of manually used systems for managing the traffic by installing the toll-booths on freeways and bridges. The design implemented in this paper attempts to capture the license plate by using three phase detection process that helps to increase the level of security and contribute in making a sustainable city. Originality/value This paper presents a distinctive approach to detect the license plate of the vehicles using the various image processing techniques such as dilation, grey-scale conversion, edge processing, etc. and finding the region of interest of the segmented image to capture the license plate of the vehicles.

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Svatiuk, Danylo, Oksana Svatiuk, and Oleksandr Belei. "APPLICATION OF THE CONVOLUTIONAL NEURAL NETWORKS FOR THE SECURITY OF THE OBJECT RECOGNITION IN A VIDEO STREAM." Cybersecurity: Education, Science, Technique 4, no. 8 (2020): 97–112. http://dx.doi.org/10.28925/2663-4023.2020.8.97112.

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The article is devoted to analyzing methods for recognizing images and finding them in the video stream. The evolution of the structure of convolutional neural networks used in the field of computer video flow diagnostics is analyzed. The performance of video flow diagnostics algorithms and car license plate recognition has been evaluated. The technique of recognizing the license plates of cars in the video stream of transport neural networks is described. The study focuses on the creation of a combined system that combines artificial intelligence and computer vision based on fuzzy logic. To solve the problem of license plate image recognition in the video stream of the transport system, a method of image recognition in a continuous video stream with its implementation based on the composition of traditional image processing methods and neural networks with convolutional and periodic layers is proposed. The structure and peculiarities of functioning of the intelligent distributed system of urban transport safety, which feature is the use of mobile devices connected to a single network, are described. A practical implementation of a software application for recognizing car license plates by mobile devices on the Android operating system platform has been proposed and implemented. Various real-time vehicle license plate recognition scenarios have been developed and stored in a database for further analysis and use. The proposed application uses two different specialized neural networks: one for detecting objects in the video stream, the other for recognizing text from the selected image. Testing and analysis of software applications on the Android operating system platform for license plate recognition in real time confirmed the functionality of the proposed mathematical software and can be used to securely analyze the license plates of cars in the scanned video stream by comparing with license plates in the existing database. The authors have implemented the operation of the method of convolutional neural networks detection and recognition of license plates, personnel and critical situations in the video stream from cameras of mobile devices in real time. The possibility of its application in the field of safe identification of car license plates has been demonstrated.

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Shevchenko, V., V. Bredikhin, T. Senchuk, and V. Verbytska. "COMPARISON OF METHODS FOR AUTOMATIC LICENSE NUMBER RECOGNITION." Municipal economy of cities 4, no. 171 (October 17, 2022): 7–11. http://dx.doi.org/10.33042/2522-1809-2022-4-171-7-11.

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The paper is devoted to the problem of automatic detection and recognition of license plates, the solution of which has many potential applications, from security to traffic management. The purpose of this work was to compare the methods of finding and recognizing car number plates, based on the application of deep learning algorithms, which takes into account different regional standards of car number plates, video quality, different speeds of vehicles, the location of the camera in relation to the vehicle license plate, defects of the car number plate (pollution , deformation), as well as changes in external lighting conditions. The advantages and disadvantages of localization and segmentation of car number plates on cars using image binarization, Viola–Jones and Harr methods are given. It was determined that adaptive approaches are better due to the possibility of compensating the impact of obstacles on different areas of the image, for example, the distribution of shadows due to the heterogeneity of illumination. It was determined that many methods in real algorithms rely directly or indirectly on the presence of number limits. Even if the limits are not used when the number is determined, they have the possibility to be used for further analysis. The methods of templates, image histograms, and contour analysis were compared to identify familiar features in the image (segmentation). It is shown that an effective approach for recognition of car license plates can be based on the application of the methods of Viola-Jones, Harr, the analysis of brightness histograms and the SVM method. Formulated conclusions on the effectiveness of the implementation of each of the procedures were confirmed as a result of conducting experiments with the developed software in the python 3 language using the cv2 computer vision library. The described approach makes it possible to obtain a fairly high recognition accuracy at different angles of rotation of the license plate relative to the camera. Keywords: automatic recognition, license plates, localization, normalization, segmentation, character recognition.

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Manoj Prakash, P., Sreerag Premanathan, ShivamKumar Surwase, M. S. Antony Vigil, and Shivam Bohare. "License Plate Detection and Facial Analysis Using TensorFlow Deep Learning Algorithm." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3246–51. http://dx.doi.org/10.1166/jctn.2019.8171.

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Nowadays license plate recognition has been applied in car access control, toll collection and other applications. Even though they exist, car thefts and illegal use of other proprietor’s license plate remain a problem. To deal with this, a computational programmed controlled framework is being developed. Also, facial analysis algorithm is implemented so as to create awareness among the common public. The way forward is to use an improved technology combination of License Plate Detection and Facial Analysis using artificial intelligence, in which vehicle image is captured by high resolution CCD camera and the license plate region is determined by image processing algorithms and facial analysis is done by using FaceNet and TensorFlow. The characters of the license plate is separated by segmentation and processed using the Canny Edge and Blob Coloring algorithm and the facial analysis is done using Facenet of TensorFlow.

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Liu, Wen Bo, and Tao Wang. "Anti-Noise Car License Plate Location Algorithm Based on Mathematical Morphology Edge Detection." Applied Mechanics and Materials 513-517 (February 2014): 1052–54. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1052.

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We proposed an improved mathematical morphology edge detection algorithm, aimed at the significance of car location in car license plate recognition system. The first step is the true color image pre-processing, an improved mathematical morphology edge detection algorithm is used to detect the edge of the car image and after the image binarization, the morphology method is used to fill the image, and then get the candidate area after corrosion expansion after open operation. Then, according to the area of the candidate region than the circumference and vertical projection used in comprehensive analysis, the license plate area can be located accurately. The experimental result shows that the method is an effective anti-noise car plate lisence location algorithm.

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Xie, Fei, Ming Zhang, Jing Zhao, Jiquan Yang, Yijian Liu, and Xinyue Yuan. "A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN." Journal of Advanced Transportation 2018 (September 26, 2018): 1–14. http://dx.doi.org/10.1155/2018/6737314.

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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. Compared with three traditional methods, the recognition accuracy of proposed algorithm has increased to 97.7% and the consuming time has decreased to 46.1ms.

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Abbass, Ghida, and Ali Marhoon. "Iraqi License Plate Detection and Segmentation based on Deep Learning." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (August 25, 2021): 102–7. http://dx.doi.org/10.37917/ijeee.17.2.12.

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Nowadays, the trend has become to utilize Artificial Intelligence techniques to replace the human's mind in problem solving. Vehicle License Plate Recognition (VLPR) is one of these problems in which the computer outperforms the human being in terms of processing speed and accuracy of results. The emergence of deep learning techniques enhances and simplifies this task. This work emphasis on detecting the Iraqi License Plates based on SSD Deep Learning Algorithm. Then Segmenting the plate using horizontal and vertical shredding. Finally, the K-Nearest Neighbors (KNN) algorithm utilized to specify the type of car. The proposed system evaluated by using a group of 500 different Iraqi Vehicles. The successful results show that 98% regarding the plate detection, and 96% for segmenting operation.

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Onesimu, J. Andrew, Robin D.Sebastian, Yuichi Sei, and Lenny Christopher. "An Intelligent License Plate Detection and Recognition Model Using Deep Neural Networks." Annals of Emerging Technologies in Computing 5, no. 4 (October 1, 2021): 23–36. http://dx.doi.org/10.33166/aetic.2021.04.003.

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One of the largest automotive sectors in the world is India. The number of vehicles traveling by road has increased in recent times. In malls or other crowded places, many vehicles enter and exit the parking area. Due to the increase in vehicles, it is difficult to manually note down the license plate number of all the vehicles passing in and out of the parking area. Hence, it is necessary to develop an Automatic License Plate Detection and Recognition (ALPDR) model that recognize the license plate number of vehicles automatically. To automate this process, we propose a three-step process that will detect the license plate, segment the characters and recognize the characters present in it. Detection is done by converting the input image to a bi-level image. Using region props the characters are segmented from the detected license plate. A two-layer CNN model is developed to recognize the segmented characters. The proposed model automatically updates the details of the car entering and exiting the parking area to the database. The proposed ALPDR model has been tested in several conditions such as blurred images, different distances from the cameras, day and night conditions on the stationary vehicles. Experimental result shows that the proposed system achieves 91.1%, 96.7%, and 98.8% accuracy on license plate detection, segmentation, and recognition respectively which is superior to state-of-the-art literature models.

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Dr. Govind Shah. "An Efficient Traffic Control System and License Plate Detection Using Image Processing." International Journal of New Practices in Management and Engineering 6, no. 01 (March 31, 2017): 20–25. http://dx.doi.org/10.17762/ijnpme.v6i01.52.

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Automatic license plate recognition is extracted from license plate of the vehicle. It is taken as an image or a continuous image taken in sequence. The extracted information can be with or without a database in many applications like electronic payment systems and freeway and arterial monitoring devices for traffic surveillance. ALPR employs CC camera, advanced camera or black and white, color camera to capture the image. ALPR is fruitful if the captured images are of good quality. ALPR is a real time application that processes the images of license plates in various conditions like dark or bright times in a day. A general technique should be identified to process images in many different countries or states. We should know that the license plate generally consists of various colors, languages, fonts and others have images in the background. Also, these plates are obstructed by mud, light, some accessories especially on a car. Here, we discuss about methods for ALPR. We classify ALPR based on the features they are used in each method and knowing their advantages, disadvantages, recognition accuracy and processing speed. Managing the timing in traffic controlling by calculating the density of an image.

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Islam, Dilshad, Tanjim Mahmud, and Tanjia Chowdhury. "An efficient automated vehicle license plate recognition system under image processing." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (February 1, 2023): 1055. http://dx.doi.org/10.11591/ijeecs.v29.i2.pp1055-1062.

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<span lang="EN-US">An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collection, border security, traffic violence detection and surveillance applications. This paper presents a methodology that is quite simple but at the same time very much efficient and this system consists of four sequential modules which are preprocessing, number plate extraction, number plate character segmentation and character recognition. Preprocessing aims to improve the image quality that is captured in various illumination conditions and stick out outstanding information that we need, which is favorable to subsequent processing including extraction, segmentation and recognition. After preprocessing various morphological operations are applied to extract the desired license plate region. Then for segmentation the bounding box method is applied that segments each letter and number present on the license plate region. Finally, template matching is applied in identifying all segmented characters present in the license plate image. The experimental results showed that the proposed system can recognize license plate characters efficiently with higher accuracy. Using MATLAB software, the proposed method attains recognition accuracy of 94.17%.</span>

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Shrivastava, Mudit, Rahul Jadhav, Pranjal Singhal, and Savita R. Bhosale. "Vehicle Owner Recognition and Speed Estimation through LPD using Deep learning (VORSELD)." ITM Web of Conferences 40 (2021): 01005. http://dx.doi.org/10.1051/itmconf/20214001005.

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As name characterizes understanding of a number plate accordingly, from past decades the use vehicles expanded rapidly, taking into account of this such a majority number of issues like overseeing and controlling trafficante keeping watch on autos and managing parking area zones to overcome this tag recognizer programming is required. The proposed work aims to detect speed of a moving vehicle through its license plate. It will fetch vehicle owner details with the help of CNN model. In this project the main focus is to detect a moving car whenever it crosses dynamic markings. It uses Tensor-flow with an SSD object detection model to detect cars and from the detection in each frame the license plate gets detected and each vehicle can be tracked across a video and can be checked if it crossed the markings made in program itself and hence speed of that vehicle can be calculated. The detected License plate will be forwarded to trained model where PyTesseract is used, which will convert image to text.

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Hashem, Noor M., and Heba Kh Abbas. "Automatic Detection and Recognition of Car Plates Based on Cascade Classifier." Ibn AL-Haitham Journal For Pure and Applied Sciences 36, no. 1 (January 21, 2023): 130–38. http://dx.doi.org/10.30526/36.1.2895.

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The study consists of video clips of all cars parked in the selected area. The studied camera height is1.5 m, and the video clips are 18video clips. Images are extracted from the video clip to be used for training data for the cascade method. Cascade classification is used to detect license plates after the training step. Viola-jones algorithm was applied to the output of the cascade data for camera height (1.5m). The accuracy was calculated for all data with different weather conditions and local time recoding in two ways. The first used the detection of the car plate based on the video clip, and the accuracy was 100%. The second is using the clipped images stored in the positive file, based on the training file (XML file), where the accuracy was 99.8%.

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Li, Hui, Peng Wang, and Chunhua Shen. "Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks." IEEE Transactions on Intelligent Transportation Systems 20, no. 3 (March 2019): 1126–36. http://dx.doi.org/10.1109/tits.2018.2847291.

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Ummadisetti, Ganesh Naidu, R. Thiruvengatanadhan, Satyala Narayana, and P. Dhanalakshmi. "Character level vehicle license detection using multi layered feed forward back propagation neural network." Bulletin of Electrical Engineering and Informatics 12, no. 1 (February 1, 2023): 293–302. http://dx.doi.org/10.11591/eei.v12i1.4010.

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Real-world traffic situations, including smart traffic monitoring, automated parking systems, and car services are increasingly using vehicle license detection systems (VLDS). Vehicle license plate identification is still a challenge with current approaches, particularly in more complicated settings. The use of machine learning and deep learning algorithms, which display improved classification accuracy and resilience, has been a significant recent breakthrough. Deep learning-based license plate identification using neural networks is proposed in this article. The number plate is detected using a multi layered feed forward back propagation neural network (MLFFBPNN). In this method, there are 3 layers namely input, hidden, and output layers has been utilized. Each layer has been related with interconnection weights. In feed forward of information, initially a set of randomly chosen weights are feed to the input data and an output has been determined. Back propagation training algorithm is utilized to train the network. Then character level identification is performed. The suggested proposed method is compared to the region-based convolutional neural network (RCNN) method in terms of accuracy and computational efficiency. The proposed method produced the character level recognition accuracy of 89%. It is improved by 4% when compared with the RCNN recognition method.

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Deshpande, Meena, M. B. Veena, and Alachew Wubie Ferede. "Auditory Speech Based Alerting System for Detecting Dummy Number Plate via Video Processing Data sets." Computational Intelligence and Neuroscience 2022 (September 2, 2022): 1–12. http://dx.doi.org/10.1155/2022/4423744.

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Spectrum of applications in computer vision use object detection algorithms driven by the power of AI and ML algorithms. State of art detection models like faster Region based convolutional Neural Network (RCNN), Single Shot Multibox Detector (SSD), and You Only Look Once (YOLO) demonstrated a good performance for object detection, but many failed in detecting small objects. In view of this an improved network structure of YOLOv4 is proposed in this paper. This work presents an algorithm for small object detection trained using real-time high-resolution data for porting it on embedded platforms. License plate recognition, which is a small object in a car image, is considered for detection and an auditory speech signal is generated for detecting fake license plates. The proposed network is improved in the following aspects: Training the classifier by using positive data set formed from the core patterns of an image. Training YOLOv4 by the features obtained by decomposing the image into low frequency and high frequency. The resultant values are processed and demonstrated via a speech alerting signals and messages. This contributes to reducing the computation load and increasing the accuracy. Algorithm was tested on eight real-time video data sets. The results show that our proposed method greatly reduces computing effort while maintaining comparable accuracy. It takes 45 fps to detect one image when the input size is 1280 × 960, which could keep a real-time speed. Proposed algorithm works well in case of tilted, blurred, and occluded license plates. Also, an auditory traffic monitoring system can reduce criminal attacks by detecting suspicious license plates. The proposed algorithm is highly applicable for autonomous driving applications.

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Song, Moon Kyou, and Md Mostafa Kamal Sarker. "Modeling and Implementing Two-Stage AdaBoost for Real-Time Vehicle License Plate Detection." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/697658.

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License plate (LP) detection is the most imperative part of the automatic LP recognition system. In previous years, different methods, techniques, and algorithms have been developed for LP detection (LPD) systems. This paper proposes to automatical detection of car LPs via image processing techniques based on classifier or machine learning algorithms. In this paper, we propose a real-time and robust method for LPD systems using the two-stage adaptive boosting (AdaBoost) algorithm combined with different image preprocessing techniques. Haar-like features are used to compute and select features from LP images. The AdaBoost algorithm is used to classify parts of an image within a search window by a trained strong classifier as either LP or non-LP. Adaptive thresholding is used for the image preprocessing method applied to those images that are of insufficient quality for LPD. This method is of a faster speed and higher accuracy than most of the existing methods used in LPD. Experimental results demonstrate that the average LPD rate is 98.38% and the computational time is approximately 49 ms.

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Ugwu, Ejiofor Martins, Onate Egerton Taylor, and Nuka Dumle Nwiabu. "An Improved Visual Attention Model for Automated Vehicle License Plate Number Recognition Using Computer Vision." European Journal of Artificial Intelligence and Machine Learning 1, no. 3 (May 25, 2022): 15–21. http://dx.doi.org/10.24018/ejai.2022.1.3.10.

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The role of an automatic licensed plate detection system (ALPDS) cannot be over-emphasized in the world today. The need for an automated system for vehicle license plate number recognition is important for security challenges. Therefore, this paper provides a smart system for vehicle license number recognition using Computer Vision. The system was trained using images of vehicles license numbers as training data. The training images were first annotated using the Visual Graphic Generator (VGG) annotation tool, after the annotation process, the trained images were pre-processed using the OpenCV library for conversion and masking of images. TesseractOCR was then used in extracting just texts from the images. The pre-processed and segmented images were then used in training the Mask R-CNN from a pre-trained weight. The result of the proposed system shows how the Mask R-CNN model was trained in ten training steps. The mask R-CNN model obtained accuracy and a loss value for each training step. The mask R-CNN model was evaluated using both training and test data. For the training and testing data, the Mask R-CNN was evaluated in terms of accuracy and loss. The evaluation was done using graphs. The results from the graph show that the Mask R-CNN had a better accuracy result in both training and testing data. The accuracy for training data was that of 95.25% and the accuracy for the testing data was 97.69%. For real-time vehicle license plate number recognition, we deployed our proposed model to the web. Here, we built a web application that allows real-time surveillance video. Our model was tested on different vehicles in the car park. The result of the mask R-CNN on the test shows how the Mask R-CNN model was used in not just capturing and extracting the vehicle’s license plate number but predicting the characters that appeared on the vehicle’s license plate number. We also compared our proposed system with another existing system. The comparison was done in terms of accuracy, loss, and precision. The result of our proposed model gave us an accuracy of 97.69%, which is higher than the existing system (85%). This study can further be improved by using the Internet of Things in performing live video streaming and also providing a database system that will be storing the predicted vehicle numbers for cars that are detected.

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Wang, Wanwei, Jun Yang, Min Chen, and Peng Wang. "A Light CNN for End-to-End Car License Plates Detection and Recognition." IEEE Access 7 (2019): 173875–83. http://dx.doi.org/10.1109/access.2019.2956357.

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Xiang, Zhijun, and Jianjun Pan. "Design of Intelligent Parking Management System Based on ARM and Wireless Sensor Network." Mobile Information Systems 2022 (September 27, 2022): 1–13. http://dx.doi.org/10.1155/2022/2965638.

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Motor vehicles are changing the way people live, but they are also putting a huge strain on urban traffic. In the majority of major cities, parking has become the number one problem for car owners to get around. The management efficiency of car parks directly affects the traffic of the whole city. In order to improve the management efficiency of the car park, this paper designs an intelligent parking management system based on ARM and ZigBee wireless sensor network. Firstly, according to the internal environment and economic cost of the car park, ultrasonic sensors are used to monitor whether the parking space is empty or not. The information collected by the ultrasonic sensors is transmitted to the ARM host controller through the ZigBee wireless sensor network, and the ARM host controller determines whether there are free parking spaces based on the collected information. Secondly, Faster R-CNN, a deep learning algorithm, is selected as the license plate recognition model, and the Faster R-CNNN is improved by the residual module. Finally, in order to extend the lifetime of the ZigBee wireless network, the ZigBee routing algorithm is investigated, and an improved routing algorithm based on energy averaging is proposed. The effectiveness of the improved routing algorithm is demonstrated by a simulation analysis through NS2. The test results show that the designed intelligent parking management system is able to complete the functions of parking space detection and license plate recognition normally, thus effectively improving the efficiency of the car park and providing great convenience to motorists.

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Bachtiar, Mochamad Mobed, Sigit Wasista, and Mukhammad Syarifudin Hidayatulloh. "Segmentation Plate and Number Vehicle using Integral Projection." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 3, no. 1 (January 31, 2018): 1–5. http://dx.doi.org/10.25139/inform.v3i1.633.

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Ticket system on the shopping mall and offices have a way to write it by the manual and automatic way. Most of the systems in use are by manual rather than automatic. With the problem, the manual system will be replaced with an automatic system that can recognize the number on the license plate. One method used to detect the plate and number that can be used is the method of finding contour. Finding contour can locate the number plate by detecting the rectangular shape of an image. This method is very important to be able to detect the number plate because there are many forms contained in the vehicle. Segmentation is used to recognize the characters on the car number plate using integral projection. Integral projection can separate the characters contained on the car number plate to facilitate processing on character recognition. The successful use of this method is 98%. errors are usually caused by the faded car plate colors

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Voronova, Larissa V., and Elena V. Panisheva. "ON THE QUESTION ON METHOD SELECTION OF THE EDGE DETECTION AND GRAPHIC OBJECT RECOGNITION APPLIED TO THE TASK OF LICENCE PLATE IDENTIFICATION." Technologies & Quality 56, no. 2 (August 25, 2022): 46–50. http://dx.doi.org/10.34216/2587-6147-2022-2-56-46-50.

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The method of the edge detection and graphic object recognition – licence plate is presented in this article. The development and use of an automatic car number recognition system is an urgent task, as it allows you to control the access of a car to a closed protected area without the participation of an operator. The article presents a comparative analysis of the quality and efficiency of various methods (Viola–Jones object detection framework, Canny edge detector, Sobel operator). The authors proposed a modification of the method for determining boundaries within the framework of the problem being solved, quantified the accuracy of recognition.

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Zhang, Guiqing, Wei Xue, Chenlu Tian, Xiaoqian Liu, Yong Li, and Leilei Pan. "Research on Vehicle Track Tracking and Vehicle Reverse Lookup Algorithm Based on Ultrasonic Waveform Recording." MATEC Web of Conferences 173 (2018): 02012. http://dx.doi.org/10.1051/matecconf/201817302012.

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In order to solve the problems of garage expand, video based searching costs high, ultrasonic detector based searching cannot achieve. This paper presents a vehicle trajectory tracking algorithm based on ultrasonic waveform recording. Parking management system can determine the parking position through the ultrasonic detector, so as to achieve vehicle reverse lookup. The system first uses the vehicle license plate recognition system to obtain the vehicle information, and sets the ultrasonic detector at the intersection to perceive the detection time and waveform recording signal. Firstly according to the time series determine the direction of the vehicle at the intersection. Then via the ultrasonic wave curve of two directions at the intersection to Identify the vehicle profile and determine the turning direction. Finally, the car's parking position is tracked through the parking space detector. This paper developed the WeChat public number for vehicle reverse lookup and payment. The user concerned to find car only need to enter the license plate number and can pay online fare. The system has been tested to be stable and reliable, which is a feasible solution to realize vehicle reverse searching at low cost.

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Platonenko, Artem, Volodymyr Sokolov, Pavlo Skladannyi, and Heorhii Oleksiienko. "TECHNICAL MEANS OF AIRINTELLIGENCE TO ENSURE THE PHYSICAL SECURITY OF INFORMATION ACTIVITIES." Cybersecurity: Education, Science, Technique 12, no. 4 (June 24, 2021): 143–50. http://dx.doi.org/10.28925/2663-4023.2021.12.143150.

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This article is devoted to highlighting the real practical capabilities of UAV thermal imaging cameras, which allow you to effectively and safely identify potentially dangerous objects that may threaten the object of information activities, or the safety of citizens or critical infrastructure of Ukraine. Based on many years of flight experience and training of specialists for private and public institutions, it was decided to compare the quality characteristics and capabilities of detection, recognition and identification of objects using modern unmanned vehicles. To ensure public safety and control of the territory, there are models with multiple optical zoom, which from a distance of 500 m allow to recognize the license plate of the car, or versions with thermal imager, which in night can help see the car, the temperature difference against other cars, and the fact that a person comes out of it. Test flights were performed at altitudes from 15 to 100 m, in the open, without the presence of bushes, trees or obstacles. Depending on the camera model and weather conditions, the figures obtained may differ significantly. The main advantages and differences in the quality of thermal imaging cameras for UAVs are described. The quality of the obtained image is demonstrated on real examples and under the same conditions. A number of requirements have been developed for shooting a quadcopter with thermal imagers of objects such as a car and a person from different heights, according to Johnson's criteria, and a work plan has been developed for further research to prepare and provide effective recommendations for pilots using this technique territories of objects of information activity and during performance of service in air reconnaissance units of law enforcement agencies of Ukraine.

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A., Swapnil, and M. S. Deshpande. "Car License Plate Detection." International Journal of Computer Applications 128, no. 13 (October 15, 2015): 8–11. http://dx.doi.org/10.5120/ijca2015906716.

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Omran, Safaa S., and Jumana A. Jarallah. "Iraqi Car License Plate Recognition Using OCR." Cihan University-Erbil Scientific Journal 2017, Special-1 (2017): 13–24. http://dx.doi.org/10.24086/cuesj.si.2017.n1a2.

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., M. Bhargavi, and Sajja Radharani . "Car License Plate Detection Using Veda." International Journal of Scientific Research in Computer Science and Engineering 5, no. 6 (December 31, 2017): 19–26. http://dx.doi.org/10.26438/ijsrcse/v5i6.1926.

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Herusutopo, Antonius, Rizky Zuhrudin, Willy Wijaya, and Yuka Musiko. "RECOGNITION DESIGN OF LICENSE PLATE AND CAR TYPE USING TESSERACT OCR AND EmguCV." CommIT (Communication and Information Technology) Journal 6, no. 2 (October 31, 2012): 76. http://dx.doi.org/10.21512/commit.v6i2.573.

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The goal of the research is to design and implement software that can recognize license plates and car types from images. The method used for the research is soft computing using library of EmguCV. There are four phases in creating the software, i.e., input image process, pre-processing, training processing and recognition. Firstly, user enters the car image. Then, the program reads and does pre-processing the image from bitmap form into vector. The next process is training process, which is learning phase in order the system to be able recognize an object (in this case license plate and car type), and in the end is the recognition process itself. The result is data about the car types and the license plates that have been entered. Using simulation, this software successfully recognized license plate by 80.223% accurate and car type 75% accurate.Keywords: Image; Pre-Processing; License plate and Car Type Recognition, Training

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Kim, Kwang-Baek, and Dae-Su Kim. "Recognition of Car License Plates Using Morphological Information and SOM Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 385–89. http://dx.doi.org/10.20965/jaciii.2004.p0385.

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In this paper, we propose car license plate recognition using morphological information and a self-organizing map (SOM) algorithm. Morphological information on horizontal and vertical edges was used to extract the license plate from a car image. A 4-directional contour tracking algorithm was applied to extract the specific area, including characters, from an extracted plate. Recognition of extracted character strings was studied using the SOM algorithm. We used 50 car images to evaluate performance. Extraction for character strings by the proposed method showed better results than that from conventional color information on RGB and HSI. License plate recognition using the SOM algorithm was very efficient.

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ABDULHAMID, Mohanad, and Njagi KINYUA. "SOFTWARE FOR RECOGNITION OF CAR NUMBER PLATE." Applied Computer Science 16, no. 1 (March 30, 2020): 73–84. http://dx.doi.org/10.35784/acs-2020-06.

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The purpose of this paper is to design and implement an automatic number plate recognition system. The system has still images as the input, and extracts a string corresponding to the plate number, which is used to obtain the output user data from a suitable database. The system extracts data from a license plate and automatically reads it with no prior assumption of background made. License plate extraction is based on plate features, such as texture, and all characters segmented from the plate are passed individually to a character recognition stage for reading. The string output is then used to query a relational database to obtain the desired user data. This particular paper utilizes the intersection of a hat filtered image and a texture mask as the means of locating the number plate within the image. The accuracy of location of the number plate with an image set of 100 images is 68%.

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Shapiro, Vladimir, Georgi Gluhchev, and Dimo Dimov. "Towards a Multinational Car License Plate Recognition System." Machine Vision and Applications 17, no. 3 (May 25, 2006): 173–83. http://dx.doi.org/10.1007/s00138-006-0023-5.

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Teng, Xiu Hua. "The Application of Image Processing Technology in the Intelligent Transportation System." Applied Mechanics and Materials 543-547 (March 2014): 2678–80. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2678.

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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

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Ning, Yuan, Yao Wen Liu, Yan Bin Zhang, and Hao Yuan. "Extraction of License Plate Region in License Plate Recognition System." Applied Mechanics and Materials 441 (December 2013): 655–59. http://dx.doi.org/10.4028/www.scientific.net/amm.441.655.

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Extraction of License plate region is an important stage in the intelligent vehicle license plate recognition system. A practical license plate extraction algorithm based on edge detection and mathematical morphology is presented, the algorithm mainly consists of six modules: pre-processing, edge detection, binaryzation and denoising, morphology operation, filtration of connected regions, finding license plate region. From the experiments, the algorithm can detect the region of license plate quickly with 98% average accuracy of locating vehicle license plate region.

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Wijaya, Marvin Chandra. "Research of Indonesian license plates recognition on moving vehicles." EUREKA: Physics and Engineering, no. 6 (November 29, 2022): 185–98. http://dx.doi.org/10.21303/2461-4262.2022.002424.

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The recognition of the characters in the license plate has been widely studied, but research to recognize the character of the license plate on a moving car is still rarely studied. License plate recognition on a moving car has several difficulties, for example capturing still images on moving images with non-blurred results. In addition, there are also several problems such as environmental disturbances (low lighting levels and heavy rain). In this study, a novel framework for recognizing license plate numbers is proposed that can overcome these problems. The proposed method in this study: detects moving vehicles, judges the existence of moving vehicles, captures moving vehicle images, deblurring images, locates license plates, extracts vertical edges, removes unnecessary edge lines, segments license plate locations, Indonesian license plate cutting character segmenting, character recognition. Experiments were carried out under several conditions: suitable conditions, poor lighting conditions (dawn, evening, and night), and unfavourable weather conditions (heavy rain, moderate rain, and light rain). In the experiment to test the success of the license plate number recognition, it was seen that the proposed method succeeded in recognizing 98.1 % of the total images tested. In unfavorable conditions such as poor lighting or when there are many disturbances such as rain, there is a decrease in the success rate of license plate recognition. Still, the proposed method's experimental results were higher than the method without deblurring by 1.7 %. There is still unsuccessful in recognizing license plates from the whole experiment due to a lot of noise. The noise can occur due to unfavourable environmental conditions such as heavy rain.

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Chen, Chun Yu, Bao Zhi Cheng, Xin Chen, Fu Cheng Wang, and Chen Zhang. "Application of Image Processing to the Vehicle License Plate Recognition." Advanced Materials Research 760-762 (September 2013): 1638–41. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1638.

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At present, the traffic engineering and automation have developed, and the vehicle license plate recognition technology need get a corresponding improvement also. In case of identifying a car license picture, the principle of automatic license plate recognition is illustrated in this paper, and the processing is described in detail which includes the pre-processing, the edge extraction, the license plate location, the character segmentation, the character recognition. The program implementing recognition is edited by Matlab. The example result shows that the recognition method is feasible, and it can be put into practice.

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Madhan, S., and M. Pradeep. "IMAGE PROCESSING OF ANDROID-BASED PATROL ROBOT FEATURING AUTOMATIC LICENSE PLATE RECOGNITION." International Journal of Students' Research in Technology & Management 3, no. 3 (September 27, 2015): 296–301. http://dx.doi.org/10.18510/ijsrtm.2015.336.

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This work develops an Android-based robot featuring automatic license plate recognition and automatic license plate patrolling. The automatic license plate recognition feature combines 4 self-developed novel methods, Wiener deconvolution vertical edge enhancement, AdaBoost plus vertical-edge license plate detection, vertical edge projection histogram segmentation stain removal, and customized optical character recognition. Besides, the automatic license plate patrolling feature also integrates 3 novel methods, HL2-band rough license plate detection, orientated license plate approaching, and Ad-Hoc-based remote motion control. Implementation results show the license plate detection rate and recognition rate of the Android-based robot are over 99% and over 98%, respectively, under various scene conditions. Especially, the execution time of license plate recognition, including license plate detection, is only about 0.7 second per frame on the Android-based robot.

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Abbass, Ghida Yousif, and Ali Fadhil Marhoon. "Car license plate segmentation and recognition system based on deep learning." Bulletin of Electrical Engineering and Informatics 11, no. 4 (August 1, 2022): 1983–89. http://dx.doi.org/10.11591/eei.v11i4.3434.

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Artificial intelligence techniques and computer vision techniques dealt with the issue of automatic license plate recognition (ALPR) that has many applications in important research field. In this paper, the method of recognizing the license plates of Iraqi cars was applied based on deep learning techniques convolutional neural network (CNN). The two database built to identifying Iraqi car plates. First database includes 2000 images of Arabic numbers and Arabic letters. Second database conations 1200 images of the Arabic names for Iraqi governorates. This paper used image-processing techniques to segmenting the numbers, letters and words from the car license plate images and then convert them into two databases that used to train the two CNN. These training CNN used to recognizing the vocabulary of the car license plate. The success rate of the numbers, letters and words recognition was 98%. The overall rate of success of this proposed system in all stages was 97%.

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Singh, Sneh Kanwar, Dr Raman Maini, and Dr Dhavlessh Ratan. "Automatic Number Plate Recognition System using Connected Component Analysis and Convolutional Neural Network." International Journal of Engineering and Advanced Technology 11, no. 1 (October 30, 2021): 167–73. http://dx.doi.org/10.35940/ijeat.f1636.1011121.

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Technology is becoming constantly important for customers. Automatic number plate Recognition (ANPR) is a device which enables the identification of a number plate in real time. For an intelligent car service, ANPR helps to promote growth, customize the classic app and increase consumer and employee productivity. Within the specification, the principal function of ANPR lies of removing the characteristics from an illustration of a license plate. An application that enables customers to display automobile repairs through the license platform number only derived from a loaded picture is augmented by a smart car service. Technological progress is that, so it is thought that improvement is important in this region too, so the best choice for automotive services is a smart car company. This work proposed a methodology to detect the numbers from car license plate using convolutional neural network. In the preprocessing of photographs on license plates, the WLS and FFT filters were included. The images are then fed into the convolutional trainings neural network. On more plates and tests is reported during the testing. Therefore, the findings indicate that the proposed solution can be taken in less time from the license model to accurately identify the characters. The experimental result shows the significance of proposed research by achieving an accuracy of 98% for the localization and true recognition of license plates from the video frames.

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Lee, Rong Choi, King Chu Hung, and Huan Sheng Wang. "Real-Time Vehicle License Plate Recognition Based on Scanning and 2D Haar Discrete Wavelet Transform." Applied Mechanics and Materials 284-287 (January 2013): 2402–6. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2402.

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This thesis is to approach license-plate recognition using 2D Haar Discrete Wavelet Transform (HDWT) and artificial neural network. This thesis consists of three main parts. The first part is to locate and extract the license-plate. The second part is to train the license-plate. The third part is to real time scan recognition. We select only after the second 2D Haar Discrete Wavelet Transform the image of low-frequency part, image pixels into one-sixteen, thus, reducing the image pixels and can increase rapid implementation of recognition and the computer memory. This method is to scan for car license plate recognition, without make recognition of the individual characters. The experimental result can be high recognition rate.

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Antar, R., S. Alghamdi, J. Alotaibi, and M. Alghamdi. "Automatic Number Plate Recognition of Saudi License Car Plates." Engineering, Technology & Applied Science Research 12, no. 2 (April 9, 2022): 8266–72. http://dx.doi.org/10.48084/etasr.4727.

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Automatic license plate recognition has become a significant tool as a result of the development of smart cities. During the experiment studied in the current paper, 50 images were used to detect Saudi car plates. After the preprocessing stage, the canny edge method to detect the car edges and different threshold techniques were used to reduce noise. Horizontal projection was applied in the segmentation process to split the plate. After that, a masking technique was utilized to locate and separate the region of interest in the image. OCR was applied to the processed images to read the characters and numbers in English and Arabic separately. Then, combining the English and Arabic text, after using the re-shaper for the Arabic letters. Finally, rendering of the results of text on images down the plate regions took place. The canny algorithm with projection technique with a proper preprocessing for images produces results with accuracy of 92.4% and 96% for Arabic and English language respectively.

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Catak, Muammer. "Car license plate recognition based on EKE-poisson transform." Journal of Intelligent & Fuzzy Systems 27, no. 4 (2014): 2023–28. http://dx.doi.org/10.3233/ifs-141168.

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Singh, Sweta. "Automatic Car License Plate Detection System: A Review." IOP Conference Series: Materials Science and Engineering 1116, no. 1 (April 1, 2021): 012198. http://dx.doi.org/10.1088/1757-899x/1116/1/012198.

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Isra, A. R., and A. Gokulanathan. "Vertical-Edge-Based Car-License-Plate Detection Method." IOSR Journal of Electrical and Electronics Engineering 12, no. 01 (January 2017): 01–06. http://dx.doi.org/10.9790/1676-1201020106.

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Al-Ghaili, Abbas M., Syamsiah Mashohor, Abdul Rahman Ramli, and Alyani Ismail. "Vertical-Edge-Based Car-License-Plate Detection Method." IEEE Transactions on Vehicular Technology 62, no. 1 (January 2013): 26–38. http://dx.doi.org/10.1109/tvt.2012.2222454.

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Shitu, Saifullahi Sadi, Syed Abd Rahman Syed Abu Bakar, Nura Musa Tahir, Usman Isyaku Bature, and Haliru Liman. "Efficient Thinning Algorithm for Malaysian Car Plate Character Recognition." ELEKTRIKA- Journal of Electrical Engineering 20, no. 3 (December 27, 2021): 15–25. http://dx.doi.org/10.11113/elektrika.v20n3.286.

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The thinning algorithm is one of the approaches of identifying each character printed on the car plate. Malaysian car plate characters appear in different character sizes, styles, customized printed characters etc. These variations contribute to difficulty in thinning successfully segmented and extracted license plate characters for recognition. To address these problems, an improved thinning operation for Malaysian car plate character recognition is proposed. In this algorithm, samples from segmented and extracted license plates are used for a thinning operation which is passed to Zhang-Suen thinning algorithm that could not guarantee one pixel thick and then to single pixelate algorithm that provides one pixel width of character for recognition. From the simulation, the result obtained has clearly proven to be the best for character recognition systems with least number of white pixels (777 pixels) and 0.26% redundant pixel left in the medial curve.

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Journal articles on the topic 'Car license plate detection and recognition'

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