Статті в журналах: "NUMBER PLATE RECOGNITION SYSTEM"

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Koppisetti, Harshit Surya. "Number Plate Recognition System using MATLAB." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4851–54. http://dx.doi.org/10.22214/ijraset.2021.35983.

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This paper presents a system called NPR (Number Plate Recognition) which is based on image processing and is used to detect the number plates of vehicles and process them to record the information. In a fast-growing world, it has become almost impossible to track illegal vehicles and store vehicle information. This is eventually leading to a rise in the crime rate, especially due to manual errors. The proposed system first captures the vehicle image and the vehicle number plate region is extracted using Image Segmentation in an image. The resulting data is then used to compare with the records on a database to come up with specific information like the vehicle's owner, place of registration, address, etc. Further, the system is implemented and simulated in MATLAB for studying feasibility and accuracy on a real image.

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Khaparde, Devesh, Heet Detroja, Jainam Shah, Rushikesh Dikey, and Bhushan Thakare. "Automatic Number Plate Recognition System." International Journal of Computer Applications 179, no. 49 (June 15, 2018): 26–29. http://dx.doi.org/10.5120/ijca2018917277.

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Davy, Manyika Kabuswa, Peter Jairous Banda, and Agripa Hamweendo. "Automatic vehicle number plate recognition system." Physics & Astronomy International Journal 7, no. 1 (March 28, 2023): 69–72. http://dx.doi.org/10.15406/paij.2023.07.00286.

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In simple terms, Automatic Vehicle Number Plate Recognition System (AVNPRS) can be described as a computer vision image processing technology. This technology captures images of vehicles and recognizes their number plates. As the world is becoming more sophisticated on each new day, security as well as management of transportation system have become a vital aspect in controlled places. Such places include modern campuses, shopping malls and other institutions. With today’s rate at which motor vehicles are increasing in numbers, there is need for an effective, affordable and efficient AVNPRS. Thus, this project proposes, outlines and discusses an efficient AVNPRS. The system under consideration in this project can be installed at main entrances of modern institutions. This is because the AVNPRS ensures that only authorized vehicles can automatically have access to such institutions. The AVNPRS ensures that it captures the image of the number plate once the input sensor detects the vehicle. The Sobel edge detection and Laplacian edge detection techniques are used at this critical stage. Thereafter, the Bounding box technique is used to find the number plate leading to character segmentation. After capturing, an image undergoes extraction and character investigation via the Optical Character Recognition (OCR). In addition, in achieving character recognition, matching between the computer template and segmented image is done via the OCR method. It is vital to mention that this system is sustainable as it successfully detects, recognizes and processes vehicle number plates on real images. The AVNPRS can be used for both traffic control and security. Therefore, the main aim of this piece of writing is to develop from a theoretical perspective an AVNPR system that can detect and capture vehicle number plate images.

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R, Lalitha. "Vehicle Number Plate Recognition System to Identify the Authenticated Owner of Vehicles." International Journal of Psychosocial Rehabilitation 24, no. 5 (May 25, 2020): 7102–7. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020719.

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Yaba, Hawar Hussein, and Hemin Omer Latif. "Plate Number Recognition based on Hybrid Techniques." UHD Journal of Science and Technology 6, no. 2 (September 1, 2022): 39–48. http://dx.doi.org/10.21928/uhdjst.v6n2y2022.pp39-48.

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Globally and locally, the number of vehicles is on the rise. It is becoming more and more challenging for authorities to track down specific vehicles. Automatic License Plate Recognition becomes an addition to transportation systems automation. Where the extraction of the vehicle license plate is done without human intervention. Identifying the precise place of a vehicle through its license plate number from moving images of the vehicle image is among the crucial activities for vehicle plate discovery systems. Artificial intelligence systems are connecting the gap between the physical world and digital world of automatic license plate detection. The proposed research uses machine learning to recognizing Arabic license plate numbers. An image of the vehicle number plate is captured and the detection is done by image processing, character segmentation which locates Arabic numeric characters on a number plate. The system recognizes the license plate number area and extracts the plate area from the vehicle image. The background color of the number plate identifies the vehicle types: (1) White color for private vehicle; (2) red color for bus and taxi; (3) blue color for governmental vehicle; (4) yellow color for trucks, tractors, and cranes; (5) black color for temporary license; and (6) green color for army. The recognition of Arabic numbers from license plates is achieved by two methods as (1) Google Tesseract OCR based recognition and (2) Machine Learning-based training and testing Arabic number character as K-nearest neighbors (kNN). The system has been tested on 90 images downloaded from the internet and captured from CCTV. Empirical outcomes show that the proposed system finds plate numbers as well as recognizes background color and Arabic number characters successfully. The overall success rates of plate localization and background color detection have been done. The overall success rate of plate localization and background color detection is 97.78%, and Arabic number detection in OCR is 45.56 % as well as in KNN is 92.22%.

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Khaparde, Devesh, Heet Detroja, Jainam Shah, Rushikesh Dikey, and Bhushan Thakare. "Survey on Automatic Number Plate Recognition System." International Journal of Computer Applications 180, no. 15 (January 24, 2018): 28–32. http://dx.doi.org/10.5120/ijca2018916193.

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Evans-Pughe, C. "Road watch [automatic number plate recognition system]." Engineering & Technology 1, no. 4 (July 1, 2006): 36–39. http://dx.doi.org/10.1049/et:20060402.

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Hajare, Gayatri, Utkarsh Kharche, Pritam Mahajan, and Apurva Shinde. "Automatic Number Plate Recognition System for Indian Number Plates using Machine Learning Techniques." ITM Web of Conferences 44 (2022): 03044. http://dx.doi.org/10.1051/itmconf/20224403044.

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India being a country where the population is above 1.3 billion where each person has at least one car of his/her use. Considering this, the number of cars driven on the roads of India must be greater than the population of the people in the country. India being a diverse country, diversity is not only seen in the language of the number plates but also in size, spacing between the letters on the number plate and the font of the number plate. Diversity differs from state to state. Even though most of the people are using English Number plates, there is no certain law as to how a number plate should be, so some people tend to have number plates according to their preferences. To withstand these problems, we have created a system using You Only Look Once version 5 (YOLOv5) for number plate detection and Google Tesseract for Character Recognition.

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Vardhan, Gotham Rishi, Kola Sunil Goud, Krovvidi Aditya Hrudai, and S. Ramani. "RECOGNITION OF VEHICLE NUMBER PLATE USING MATLAB." International Journal of Computer Science and Mobile Computing 11, no. 1 (January 30, 2022): 108–15. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.013.

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The ANPR (Automatic range Plate Recognition) system relies on image process technology. It's one of the required systems designed to sight the vehicle range plate. In today’s world with the increasing range of cars day by day, it’s impossible to manually keep a record of the whole vehicle. With the event of this technique, it becomes simple to stay a record and use it whenever needed. The most objective here is to style associate economical automatic vehicle identification system by victimization vehicle range plate. The system initially would capture the vehicle's image as presently because the vehicle reaches the protection checking space. The captured pictures area unit is then extracted by the victimization segmentation method. Optical character recognition is employed to spot the characters. The obtained information is then compared with the information kept in their info. The system is enforced and simulated on MATLAB and performance is tested on real pictures. This kind of system is widely employed in control areas, tolling, lots, etc. this technique is principally designed for the aim of the security system.

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Kale, Tushar. "Helmet Detection and Number Plate Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 4036–40. http://dx.doi.org/10.22214/ijraset.2023.52559.

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Abstract: Developing countries have always relied on motorcycles as their primary mode of transportation, but unfortunately, the number of motorcycle accidents has been on the rise in recent years. One of the leading causes of fatalities in these accidents is the lack of helmet usage by motorcyclists. To ensure that motorcyclists wear helmets, traditional methods include manual monitoring by traffic police at intersections or the use of CCTV footage to identify those not wearing a helmet. However, these methods require significant human effort and intervention. This system proposes an automated approach to identify nonhelmeted motorcyclists and retrieve their license plate information from CCTV footage. The system first differentiates moving objects as motorcycles or non-motorcycles. For classified motorcyclists, the system identifies whether they are wearing helmets or not. If the motorcyclist is not wearing a helmet, the system extracts the license plate number using an OCR algorithm.

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Songa, Akhil, Rahul Bolineni, Harish Reddy, Sohini Korrapolu, and Vani Jayasri Geddada. "Vehicle Number Plate Recognition System Using TESSERACT-OCR." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 323–27. http://dx.doi.org/10.22214/ijraset.2022.41198.

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Abstract: With the increase in the number of vehicles, automated systems to store vehicle information are becoming increasingly necessary. Communication is critical for traffic management and crime reduction, and it cannot be overlooked. Automatic vehicle identification using number plate recognition is a reliable method of identifying vehicles. It requires a lengthy time and a lot of practice to develop satisfactory results using present algorithms that are based on the idea of learning. Even so, accuracy is not a significant concern. It has been devised as an efficient approach for recognizing vehicle number plates, which is included in the suggested algorithm. The technique is intended to address the difficulties of scaling and recognition of the position of characters as long as the accuracy is maintained. Automatic Number Plate Detection is a unique application in Machine Learning as it detects images and converts them to text form. The algorithm detects and captures the vehicle image and extracts the vehicle number plate using image segmentation. The extracted image is later sent to optical character recognition technology for character recognition. This system is implemented in areas like traffic surveillance, military zones, apartments, etc. Keywords: Optical Character Recognition, tesseract ocr, matplotlib, Number Plate Recognition.

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Palampatla, Hrithik Roshan. "Automatic Number Plate Recognition Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 25, 2021): 2394–400. http://dx.doi.org/10.22214/ijraset.2021.36889.

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Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their registration number issued by government. ANPR is often used in the detection of stolen vehicles, traffic surveillance system. Our project presents a model in which the vehicle license plate image is obtained by the digital cameras and the image is processed to get the number plate information. A vehicle image is captured and processed using various methods. Vehicle number plate region is extracted using the deep neural networks. Optical character recognition is implemented using certain machine learning algorithms for the character recognition. The system is implemented using deep neural network model, machine learning algorithms and is simulated in python, and its performance is tested on real images. It is observed that the developed model successfully detects the license plate region and recognizes the individual characters. There are various recognition strategies that have been produced and number plate recognition systems are today used in different movement and security applications, such as access and border control, parking, or tracking of stolen vehicles.

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Sugeng, Sugeng, and Eniman Yunus Syamsuddin. "Designing Automatic Number Plate Recognition (ANPR) Systems Based on K-NN Machine Learning on the Raspberry Pi Embedded System." JTEV (Jurnal Teknik Elektro dan Vokasional) 5, no. 1.1 (September 23, 2019): 19. http://dx.doi.org/10.24036/jtev.v5i1.1.106135.

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Research on vehicle number plate recognition or Automatic Number Plate Recognition (ANPR) is mostly done by researchers to produce an introduction that has high accuracy. Several methods of introduction are carried out such as introduction to edge detection and morphology, relationship analysis between objects, machine learning and deep learning. In this research a K-NN machine learning ANPR system was developed in character recognition. The method of analyzing relationships between objects is used to localize number plates. The system that was developed also added an artificial intelligence to be able to find out the fault of the number plate recognition and fix it based on the position of the character group in the number plate. The ANPR system is designed to be an Embedded system so that it can be implemented to be able to carry out the identification of two-wheeled and four-wheeled vehicle license plates. The ANPR system was also developed to be used in the parking management system. In this research the recognized number plates are limited to private number plates in Indonesia. In testing, the system is made capable of recognizing the number plates of two-wheeled vehicles and four-wheeled vehicles on vehicles that have a standard license plate according to Polri regulations, both in the font type and the number plate writing format. The results of vehicle number plate recognition reached an accuracy of 98%.

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Sandha, Somalin, and Debaraj Rana. "Automatic Car Number Plate Recognition System for Authorization." Circulation in Computer Science MCSP2017, no. 01 (September 24, 2017): 30–34. http://dx.doi.org/10.22632/ccs-2017-mcsp036.

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In present day scenario the security and authentication is very much needed to make a safety world. Beside all security one vital issue is recognition of number plate from the car for Authorization. In the busy world everything cannot be monitor by a human, so automatic license plate recognition is one of the best application for authorization without involvement of human power. In the proposed method we have make the problem into three fold, firstly extraction of number plate region, secondly segmentation of character and finally Authorization through recognition and classification. For number plate extraction and segmentation we have used morphological based approaches where as for classification we have used Neural Network as classifier. The proposed method is working well in varieties of scenario and the performance level is quiet good.

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Apte, Sharv, Shashank Chafekanade, Atharva Deore, Aneesh Deshmukh, and Isha Deshpande. "Automatic Car Number Plate Detection System Using OpenCv." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1151–53. http://dx.doi.org/10.22214/ijraset.2023.51377.

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bstract: Automatic Number Plate Recognition (ANPR) is an issue that has received a lot of attention and has a lot of successful solutions. Due to the differences in number plate features around the world, these solutions are typically tailored for a specific environment. These attributes are employed in number plate recognition algorithms; thus, a universal solution would be difficult to achieve because the image analysis techniques used to develop these algorithms cannot guarantee 100% accuracy. This research focuses on a proposed method that is optimal for all types of car number plates. The program, which is implemented in Python and uses the OpenCV library, locates the plate by combining edge detection and Feature Detection approaches with mathematical morphology. EasyOCR python package was used to identify the characters on the license plate that were detected.

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Patel, Chirag, Dipti Shah, and Atul Patel. "Automatic Number Plate Recognition System (ANPR): A Survey." International Journal of Computer Applications 69, no. 9 (May 17, 2013): 21–33. http://dx.doi.org/10.5120/11871-7665.

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., Vishnu K. B. "AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM FOR VEHICLE IDENTIFICATION." International Journal of Research in Engineering and Technology 04, no. 15 (April 25, 2015): 1–4. http://dx.doi.org/10.15623/ijret.2015.0415001.

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Tiwari, Bhawna, Archana Sharma, Malti Gautam Singh, and Bhawana Rathi. "Automatic Vehicle Number Plate Recognition System using Matlab." IOSR Journal of Electronics and Communication Engineering 11 (April 2016): 10–16. http://dx.doi.org/10.9790/2834-1104021016.

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Wang, Jia, and Wei Qi Yan. "BP-Neural Network for Plate Number Recognition." International Journal of Digital Crime and Forensics 8, no. 3 (July 2016): 34–45. http://dx.doi.org/10.4018/ijdcf.2016070103.

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The License Plate Recognition (LPR) as one crucial part of intelligent traffic systems has been broadly investigated since the boosting of computer vision techniques. The motivation of this paper is to probe in plate number recognition which is an important part of traffic surveillance events. In this paper, locating the number plate is based on edge detection and recognizing the plate numbers is worked on Back-Propagation (BP) Artificial Neural Network (ANN). Furthermore, the authors introduce the system implementation and take advantage of the well-known Matlab platform to delve how to accurately recognize plate numbers. There are 80 samples adopted to test and verify the proposed plate number recognition method. The experimental results demonstrate that the accuracy of the authors' character recognition is above 70%.

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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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Prince, Rathore, Gupta Puja, Jain Sarthak, and Shrivastava Yash. "A study of the automated vehicle number plate recognition system." i-manager’s Journal on Pattern Recognition 9, no. 2 (2022): 30. http://dx.doi.org/10.26634/jpr.9.2.19162.

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Automatic Number Plate Recognition (ANPR) uses number plates to identify vehicles. The goal of an automated vehicle identification system is to identify the vehicle based on the number plate. The system enforces the regulations, parking, etc. It can also be used at the entrance to protect a large area, such as a military zone or the region around important government buildings like the military base, Parliament, Supreme Court, etc. The smart technology recognizes and captures the image of the vehicle. The number plate area of the vehicle is extracted using image segmentation on the image. Optical character recognition is used for character recognition. The performance data may also be compared to database records to determine the car owner, enrollment location, residence, etc. The testing showed that the improved algorithm easily recognized the number plate of a vehicle on genuine photographs.

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Ahire, Pritam, Saiprasad Kadam, and Ajay Jagtap. "Image Enhancement and Automated Number Plate Recognition." International Journal of Science and Healthcare Research 8, no. 2 (April 28, 2023): 178–86. http://dx.doi.org/10.52403/ijshr.20230221.

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Automatic Number Plate Recognition (ANPR) and Vehicle Number plate detection (VNPD) systems. ANPR, is a technology that enables automatic detection, recognition, and identification of vehicle license plates, while VNPD is a subset of ANPR that focuses specifically on detecting and recognizing license plates.Several researchers have explored different approaches to ANPR and VNPD systems, as evidenced by the various papers listed. For instance, presented an e security system for vehicles number tracking at a parking lot, proposed a method of monitoring traffic signals, and violations using ANPR and GSM. In order to proceed, the conventional Grab Cut algorithm must first interactively give a candidate frame. for the target detection job to be done.To automate the identification of the licence plate by the Grab Cut algorithm, we update the candidate frame by incorporating the aspect ratio of the licence plate as the foreground extraction feature. Then, to fully implement picture noise reduction, we combined the Bernsen algorithm with the Wiener filter, which is extensively used in the fields of digital signal processing in order to increase the detection precision of conventional target identification techniques. Overall, the papers listed in the question demonstrate the wide-ranging applications of ANPR and VNPD technologies, from parking lot security to traffic signal control to driver assistance systems. These technologies have the potential to improve safety, efficiency, and security on the road, and researchers continue to explore new approaches to their development and implementation. Keywords: Detection of Number Plate, Convolutional Neural Network-(CNN), Object detection, character identification, Machine Learning-(ML).

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Roy, Siddhartha. "AUTOMATICS NUMBER PLATE RECOGNITION USING CONVOLUTION NEURAL NETWORK." Azerbaijan Journal of High Performance Computing 3, no. 2 (December 29, 2020): 234–44. http://dx.doi.org/10.32010/26166127.2020.3.2.234.244.

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used for security, safety, and also commercial aspects such as parking control access, and legal steps for the red light violation, highway speed detection, and stolen vehicle detection. The license plate of any vehicle contains a number of numeric characters recognized by the computer. Each country in the world has specific characteristics of the license plate. Due to rapid development in the information system field, the previous manual license plate number writing process in the database is replaced by special intelligent device in a real-time environment. Several approaches and techniques are exploited to achieve better systems accuracy and real-time execution. It is a process of recognizing number plates using Optical Character Recognition (OCR) on images. This paper proposes a deep learning-based approach to detect and identify the Indian number plate automatically. It is based on new computer vision algorithms of both number plate detection and character segmentation. The training needs several images to obtain greater accuracy. Initially, we have developed a training set database by training different segmented characters. Several tests were done by varying the Epoch value to observe the change of accuracy. The accuracy is more than 95% that presents an acceptable value compared to related works, which is quite satisfactory and recognizes the blurred number plate.

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Ravi Kumar, J. M. S. V., B. Sujatha, and N. Leelavathi. "Automatic Vehicle Number Plate Recognition System Using Machine Learning." IOP Conference Series: Materials Science and Engineering 1074, no. 1 (February 1, 2021): 012012. http://dx.doi.org/10.1088/1757-899x/1074/1/012012.

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Kaur, Sarbjit. "An Automatic Number Plate Recognition System under Image Processing." International Journal of Intelligent Systems and Applications 8, no. 3 (March 8, 2016): 14–25. http://dx.doi.org/10.5815/ijisa.2016.03.02.

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Babu, G. Sudhakar, and K. Chandra Sekhar Reddy. "Automatic Payment System in Tollgate Using Number Plate Recognition." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 49–51. http://dx.doi.org/10.26438/ijcse/v7i5.4951.

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OP, Shaharniya. "Automatic Moving Vehicle Detection and Number Plate Recognition System." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (July 31, 2020): 2094–108. http://dx.doi.org/10.22214/ijraset.2020.30603.

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Singh, Birmohan, Manpreet Kaur, Dalwinder Singh, and Gurwinder Singh. "Automatic number plate recognition system by character position method." International Journal of Computational Vision and Robotics 6, no. 1/2 (2016): 94. http://dx.doi.org/10.1504/ijcvr.2016.073761.

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Patel, Bhavin A., and Ashish Singhadia. "Automatic Number Plate Recognition System Using Improved Segmentation Method." International Journal of Engineering Trends and Technology 16, no. 8 (October 25, 2014): 386–89. http://dx.doi.org/10.14445/22315381/ijett-v16p277.

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Abdul Samad, Shakeeb M. A. N., Fahri Heltha, and M. Faliq. "The Study of Plate Number Recognition for Parking Security System." International Journal of Advanced Technology in Mechanical, Mechatronics and Materials 1, no. 3 (December 31, 2020): 100–107. http://dx.doi.org/10.37869/ijatec.v1i3.34.

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Анотація:

Car Plate Number Recognition System is an important platform that can be used to identify a car vehicle identity. The Recognition System is based on image processing techniques and computer vision. A webcam is used to capture an image of the car plate number from different distance, and the identification is conducted through four processes of stages: Image Acquisition Pre-processing, Extraction, Segmentation, and Character Recognition. The Acquisition Pre-processing stage is extracted the region of interest of the image. The image is captured by live video of the webcam, then converted to grayscale and binary image. The Extraction stage is extracted the plate number characters from binary image using a connected components method. In the Segmentation stage is done by implementing horizontal projection as well as moving average filter. Lastly, in the Character Recognition, is used to identify the segmented characters of the plate number using optical character recognition. The proposed method is worked well for Malaysian's private cars plate number, and can be implemented in car park system to increase level of security of the system by confirming the bar code of the parking ticket and the plate number of the car at the incoming and outgoing gates.

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Widianto, Eko Didik, Herrizal Muhammad Wijaya, and Ike Pertiwi Windasari. "RFID Based Parking System and Vehicle Plate Number Image Recognition." Jurnal Teknologi dan Sistem Komputer 5, no. 3 (July 31, 2017): 115–22. http://dx.doi.org/10.14710/jtsiskom.5.3.2017.115-122.

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The length of time in manually recording vehicle license plates in the parking system leads to long vehicle queues. This research developed automatic parking system based on RFID and vehicle plate number recognition. Authentication used a camera and Arduino Uno as the controller for RFID reading, feedback and gatekeeper control. The system will compare the character's image and RFID in the record of the database to authorize a vehicle. Image processing was done by contour analysis method and had 91% in accuracy at 60 cm of distance and 131.89 milliseconds of reading speed. The system had been able to work to automatically open and close gates based on the match of RFID and vehicle plate number recognition.

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Mudda, Avinash, P. Sashi Kiran, Ashish Kumar, and Venkata Sreenivas. "Vehicle Allowance System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1085–89. http://dx.doi.org/10.22214/ijraset.2023.50169.

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Abstract: License plate detection is an image processing technology that uses a license (number) plate for vehicle identification. The objective is to design and implement an efficient vehicle identification system that identifies the vehicle using the vehicle’s license plate. The system can be implemented at the entrance of parking lots, toll booths, or any private premises like colleges, etc. to keep records of ongoing and outgoing vehicles. It can be used to allow access to only permitted vehicles inside the premises. The developed system first captures the image of the vehicle’s front, then detects the license plate, and then reads the license plate. The vehicle license plate is extracted using image processing of the image. Optical character recognition (OCR) is used for character recognition. The system is implemented using OpenCV and its performance is tested on various images. It is observed that the developed system successfully detects and recognizes the vehicle license plate. To recognize License number plates using the Python programming language. We will utilize OpenCV for this project to identify the license number plates and the python py-tesseract for the characters and digits extraction from the plate. We will build a Python program that automatically recognizes the License Number Plate

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Singh, Mr Santosh Kumar. "Automatic Number Plate Recognition System for Vehicle Identification using Optical Character Recognition." International Journal for Research in Applied Science and Engineering Technology 7, no. 4 (April 30, 2019): 1658–62. http://dx.doi.org/10.22214/ijraset.2019.4300.

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34

Alzamora, Deanova Ghivari, Habib Saifuddin Fathoni, and Vivi Bella Callista. "Automatic Number Plate Recognition Application for Metropolitan Toll Road Payment System." Applied Mechanics and Materials 912 (February 17, 2023): 85–89. http://dx.doi.org/10.4028/p-tjbkpy.

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Many factors cause congestion on toll roads, one of which is service time at toll gates. Maximum service times for each vehicle be regulated by Indonesia Toll Authority to no more than 6 seconds for the open transaction toll system. There-fore, an electronic toll road payment system integrated with ANPR (Automatic Number Plate Recognition) technology is proposed. The proposed system can identify vehicle plates automatically to make payments. The Optical Character Recognition (OCR) method was chosen in this study for recognizing the number plate. ANPR with Tesseract OCR is placed inside the computer. Based on the results of the analysis and testing that has been carried out, the low average accuracy of 63,14% from this research is small due to the small number of sample images. The average execution times of 1405 milliseconds from this research are be-low the maximum 6 seconds limit for open transaction toll collection in Indonesia. One failed plate localization from sample images has founded in a car image with number plate DK 1547 EC.

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Tawfeeq, Furat, and Yasmine Tabra. "Gate Control System for New Iraqi License Plate." Iraqi Journal for Computers and Informatics 41, no. 1 (December 31, 2014): 1–3. http://dx.doi.org/10.25195/ijci.v41i1.89.

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This paper presents an approach to license plate localization and recognition. A proposed method is designed to control the opening of door gate based on the recognition of the license plates number in Iraq. In general the system consists of four stages; Image capturing, License plate cropping, character segmentation and character recognition. In the first stage, the vehicle photo is taken fromstandard camera placed on the door gate with a specific distance from the front of vehicle to be processed by our system. Then, the detection method searches for the matching of the license plate in the image with a standard plate. The segmentation stage is performed by is using edge detection. Then character recognition, done by comparing with template standard numbers and letters used in the Iraqi plate. The system was implemented using Matlab (R2012a) and shows accurate performance results reached 93.33%.

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Rehman, Saif Ur, Moiz Ahmad, Asif Nawaz, and Tariq Ali. "An Efficient Approach for Vehicle Number Plate Recognition in Pakistan." Open Artificial Intelligence Journal 06, no. 1 (May 9, 2020): 12–21. http://dx.doi.org/10.2174/1874061802006010012.

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Introduction: Recognition of Vehicle License Number Plates (VLNP) is an important task. It is valuable in numerous applications, such as entrance admission, security, parking control, road traffic control, and speed control. An ANPR (Automatic Number Plate Recognition) is a system in which the image of the vehicle is captured through high definition cameras. The image is then used to detect vehicles of any type (car, van, bus, truck, and bike, etc.), its’ color (white, black, blue, etc.), and its’ model (Toyota Corolla, Honda Civic etc.). Furthermore, this image is processed using segmentation and OCR techniques to get the vehicle registration number in form of characters. Once the required information is extracted from VLNP, this information is sent to the control center for further processing. Aim: ANPR is a challenging problem, especially when the number plates have varying sizes, the number of lines, fonts, background diversity, etc. Different ANPR systems have been suggested for different countries, including Iran, Malaysia, and France. However, only a limited work exists for Pakistan vehicles. Therefore, in this study, we aim to propose a novel ANPR framework for Pakistan VLNP recognition. Methods: The proposed ANPR system functions in three different steps: (i) - Number Plate Localization (NPL); (ii)- Character Segmentation (CS); and (iii)- Optical Character Recognition (OCR), involving template-matching mechanism. The proposed ANPR approach scans the number plate and instantly checks against database records of vehicles of interest. It can further extract the real=time information of driver and vehicle, for instance, license of the driver and token taxes of vehicles are paid or not, etc. Results: Finally, the proposed ANPR system has been evaluated on several real-time images from various formats of number plates practiced in Pakistan territory. In addition to this, the proposed ANPR system has been compared with the existing ANPR systems proposed specifically for Pakistani licensed number plates. Conclusion: The proposed ANPR Model has both time and money-saving profit for law enforcement agencies and private organizations for improving homeland security. There is a need to expand the types of vehicles that can be detected: trucks, buses, scooters, bikes. This technology can be further improved to detect the crashed vehicle’s number plate in an accident and alert the closest hospital and police station about the accident, thus saving lives.

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Boronov, Erkinbek, and Prof Wei Wei. "The Intelligent Vehicle Number Plate Recognition System based on Arduino." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 5481–97. http://dx.doi.org/10.22214/ijraset.2023.51501.

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Abstract: The thesis proposes the development of The Intelligent Vehicle Number Plate Recognition System (IVNPRS) using the Arduino platform. The system uses image processing techniques to automatically detect and recognize vehicle number plates, and it includes a camera module, an image processing unit, and an Arduino microcontroller. The proposed system has practical applications such as security monitoring, automatic toll collection, and traffic monitoring. The thesis discusses the different components of the system, image processing techniques used, and evaluates its performance by measuring accuracy, speed, and robustness under different lighting and weather conditions. The experimental results suggest that the VNPRS has the potential to improve vehicular traffic and safety in various locations.

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Bharathi, M., N. Padmaja, and M. Dharani. "OCR-based vehicle number plate recognition powered by a raspberry Pi." i-manager’s Journal on Electronics Engineering 12, no. 3 (2022): 33. http://dx.doi.org/10.26634/jele.12.3.18959.

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Modern technology has revolutionized automation. Security is at high priority with increasing automation. Today, to help people feel comfortable, video surveillance cameras are installed in public places like schools, hospitals, and other buildings. The main goal of this research work is to automatically collect vehicle images with a camera using a Raspberry Pi and recognising the licence plate of the vehicles. Vehicle number plate recognition is a challenging but crucial system. This is highly helpful for automating toll booths, identifying automated signal violators, and identifying traffic regulation violators. In this work, a Raspberry Pi is used for vehicle license plate recognition, which uses image processing to automatically recognize license plates. Incoming camera footage is continuously processed by the system to look for any signs of number plates. When the camera detects a number plate, Optical Character Recognition (OCR) technique is used to process the image and extract the number from it. The distance to an object is calculated by a sensor utilizing sound waves. The extracted number is then displayed by the system. This can be used for additional authentication.

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Simon, Mutua, Bernard Shibwabo, and Kaibiru Mutua. "An Automatic Number Plate Recognition System for Car Park Management." International Journal of Computer Applications 175, no. 7 (October 17, 2017): 36–42. http://dx.doi.org/10.5120/ijca2017915608.

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40

Keraf, Nui Din, Phaklen Ehkan, Virakwan Hai Kelian, Norulhuda Mohd Noor, Hafifah Darus, and Eunice Loke. "Automatic vehicle identification system using number plate recognition in POLIMAS." IOP Conference Series: Materials Science and Engineering 767 (March 21, 2020): 012056. http://dx.doi.org/10.1088/1757-899x/767/1/012056.

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41

Alam, Nur-A., Mominul Ahsan, Md Abdul Based, and Julfikar Haider. "Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks." Technologies 9, no. 1 (January 20, 2021): 9. http://dx.doi.org/10.3390/technologies9010009.

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Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.

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Fitriati, Desti, Nira Ravika Pasha, Bambang Hariyanto, Amir Murtako, and Sri Rezeki Candra Nursari. "SMART SYSTEM FOR AUTOMATIC CROP AND RECOGNITION PLAT NUMBER." Jurnal Riset Informatika 3, no. 2 (March 2, 2021): 145–52. http://dx.doi.org/10.34288/jri.v3i2.183.

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Based on data from the Central Statistics Agency in 2018, it was written that the number of motorbikes for the Indonesian region was 120.10 million or 82% and for cars 26.75 million or around 18% of the total population. With the increasing population of motorized vehicle users, it will result in an increase in problems that occur in traffic violations and also the technology security system in the parking system. Most of the existing parking systems still require parking attendants. In addition, the existing system only discusses the opening and closing of bars and providing information on parking lots. Although the existing system already uses artificial intelligence to read plate numbers, the officers are still matching it. Of course, this is not effective and efficient because the use of artificial intelligence is not purely done by the system. To overcome this, the solution given in this study is to create a parking system that can read plate numbers automatically and store vehicle entry data directly into the database. The system created can also open and close the door latch automatically. The template matching image processing technique was chosen to solve this problem. Based on the experimental results, the system can recognize plate numbers with an accuracy of 83%. For further research, it is necessary to introduce vehicle ownership and provide parking information so that the parking system becomes more perfect.

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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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Zaheen, Muhammad Yasir, Zia Mohi-u-din, Ali Akber Siddique, and Muhammad Tahir Qadri. "Exhaustive Security System Based on Face Recognition Incorporated with Number Plate Identification using Optical Character Recognition." January 2020 39, no. 1 (January 1, 2020): 145–52. http://dx.doi.org/10.22581/muet1982.2001.14.

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In recent times due to rise in terrorism, people need to live in a safer place where unidentified persons will not be allowed to enter in the premises. Securing of major areas is a vital issue that needs to be addressed for the intelligence and security agencies. At the surrounding of premises, CCTV (CloseCircuit Television) cameras are usually installed to identify the number plate from database by using OCR (Optical Character Recognition) algorithm. This method of security by identifying only vehicle without verifying the person inside it is usually causing serious security issues. Identification of a person is usually done through image processing by using Viola Jones algorithm and acquire the information of the facial components to create a dataset for machine learning. It is imperative to introduce such a system that will be capable to identify the person along with the number plate of vehicle from the stored database. In this research, a comprehensive security system based on face recognition integrated with the vehicle number plate is proposed. The combined information of both dedicated cameras is then transferred to the based station for identification. This system is capable, of securing premises from crime in a more enhanced way.

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45

Nashwan, Farhan M., Khaled A. M. Al Soufy, Nagi H. Al-Ashwal, and Majed A. Al-Badany. "Design of Automatic Number Plate Recognition System for Yemeni Vehicles with Support Vector Machine." International Journal of Intelligent Systems and Applications 15, no. 4 (August 8, 2023): 37–52. http://dx.doi.org/10.5815/ijisa.2023.04.04.

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Automatic Number Plate Recognition (ANPR) is an important tool in the Intelligent Transport System (ITS). Plate features can be used to provide the identification of any vehicle as they help ensure effective law enforcement and security. However, this is a challenging problem, because of the diversity of plate formats, different scales, rotations and non-uniform illumination and other conditions during image acquisition. This work aims to design and implement an ANPR system specified for Yemeni vehicle plates. The proposed system involves several steps to detect, segment, and recognize Yemeni vehicle plate numbers. First, a dataset of images is manually collected. Then, the collected images undergo preprocessing, followed by plate extraction, digit segmentation, and feature extraction. Finally, the plate numbers are identified using Support Vector Machine (SVM). When designing the proposed system, all possible conditions that could affect the efficiency of the system were considered. The experimental results showed that the proposed system achieved 96.98% and 99.19% of the training and testing success rates respectively.

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Shanmugaraj.S et al. "Auto Detection of Number Plate of Person without Helmet." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 3 (March 20, 2019): 21–24. http://dx.doi.org/10.17762/ijritcc.v7i3.5252.

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Automated Number Plate Recognition organization would greatly enhance the ability of police to detect criminal commotion that involves the use of motor vehicles. Automatic video investigation from traffic surveillance cameras is a fast-emerging field based on workstation vision techniques. It is a key technology to public safety, intelligent transport system (ITS) and for efficient administration of traffic without wearing helmet. In recent years, there has been an increased scope for involuntary analysis of traffic activity. It defines video analytics as computer-vision-based supervision algorithms and systems to extract contextual information from video. In traffic circumstancesnumeroussupervise objectives can be continue by the application of computer vision and pattern gratitude techniques, including the recognition of traffic violations (e.g., illegal turns and one-way streets) and the classification of road users (e.g., vehicles, motorbikes, and pedestrians). Currently most reliable approach is through the acknowledgment of number plates, i.e., automatic number plate recognition (ANPR).

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Habeeb, Dhuha, Fuad Noman, Ammar Ahmed Alkahtani, Yazan A. Alsariera, Gamal Alkawsi, Yousef Fazea, and Ammar Mohammed Al-jubari. "Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition." Computational Intelligence and Neuroscience 2021 (November 6, 2021): 1–14. http://dx.doi.org/10.1155/2021/3971834.

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Recognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obstacle, particularly in countries whose plate numbers are written in different languages or blended with Latin alphabets. This paper introduces a recognition system for Arabic and Latin alphabet license plates using a deep-learning-based approach in conjugation with data collected from two specific countries: Iraq and Malaysia. The system under study is proposed to detect, segment, and recognize vehicle plate numbers. Moreover, Iraqi and Malaysian plates were used to compare these processes. A total of 404 Iraqi images and 681 Malaysian images were tested and used for the proposed techniques. The evaluation took place under various atmospheric environments, including fog, different contrasts, dirt, different colours, and distortion problems. The proposed approach showed an average recognition rate of 85.56% and 88.86% on Iraqi and Malaysian datasets, respectively. Thus, this evidences that the deep-learning-based method outperforms other state-of-the-art methods as it can successfully detect plate numbers regardless of the deterioration level of image quality.

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48

Omran, Safaa, and Jumana Jarallah. "AUTOMATIC IRAQI CARS NUMBER PLATES EXTRACTION." Iraqi Journal for Computers and Informatics 44, no. 1 (June 30, 2018): 23–30. http://dx.doi.org/10.25195/ijci.v44i1.111.

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License Plate Recognition (LPR) system becomes animportant research issue in recent years due to its importance to wideranges of commercial applications. The first and the most importantstage for any LPR system is the localization of the number platewithin the vehicle image. This paper presents a methodology for Iraqicars number plates extraction from the vehicle image using twomethods, the first one is morphological operations and the secondmethod is edge detection. The main idea is to use these two differentmethods in such away so that the number plate of the vehicle can beextracted precisely. These algorithms can quickly and correctly detectand extract the number plate from the vehicle image although therewas a little noise in the image. This paper also makes a comparisonbetween the two methods of extraction in results. The software thatused to build the systems is MATLAB R2014a

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Chen, Zeyou, Yangyang Su, Yong Liu, Jiazhen Huang, and Wuwen Cao. "Key technologies of intelligent transportation based on image recognition." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142091727. http://dx.doi.org/10.1177/1729881420917277.

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With the development of economy, the research of urban intelligent transportation system is becoming more and more important. The research and development of plate number recognition system is an important factor to realize the intelligence and modernization of transportation system. It uses each car to have a unique plate number and recognizes the vehicle number through the vehicle image captured by the camera. On the basis of image recognition, this article takes plate number image as the research object and discusses the key technologies of plate number recognition system. First, this article uses image preprocessing technology to process images to improve image quality. Second, the plate number location algorithm based on the connected region search is analyzed. According to the characteristics of the plate number itself, the regional features of the plate number are extracted to locate the plate number accurately. Then, an improved vertical projection-based plate number character segmentation method is proposed to segment plate number characters. Finally, combined with character characteristics, the template matching method is used to recognize plate number characters. The simulation results show that, on the basis of image recognition, this article studies the key technologies of plate number recognition system, which effectively improves the performance of the system and makes the recognition of plate number more effective and accurate.

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Ganjoo, Siddharth. "YOLO and Mask R-CNN for Vehicle Number Plate Identification." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 4423–30. http://dx.doi.org/10.22214/ijraset.2022.46021.

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Abstract: License plate scanners have grown in popularity in parking lots during the past few years. In order to quickly identify license plates, traditional plate recognition devices used in parking lots employ a fixed source of light and shooting angles. For skewed angles, such as license plate images taken with ultra-wide angle or fisheye lenses, deformation of the license plate recognition plate can also be quite severe, impairing the ability of standard license plate recognition systems to identify the plate. Mask RCNN gadget that may be utilized for oblique pictures and various shooting angles. The results of the experiments show that the suggested design will be capable of classifying license plates with bevel angles larger than 0/60. Character recognition using the suggested Mask R-CNN approach has advanced significantly as well. The proposed Mask R-CNN method has also achieved significant progress in character recognition, which is tilted more than 45 degrees as compared to the strategy of employing the YOLOv2 model. Experiment results also suggest that the methodology presented in the open data plate collecting is better than other techniques (known as the AOLP dataset)

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