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Lubna, Naveed Mufti, and Syed Afaq Ali Shah. "Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms." Sensors 21, no. 9 (April 26, 2021): 3028. http://dx.doi.org/10.3390/s21093028.
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Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.
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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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Gunawan, Teddy Surya, Abdul Mutholib, and Mira Kartiwi. "Performance Evaluation of Automatic Number Plate Recognition on Android Smartphone Platform." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (August 1, 2017): 1973. http://dx.doi.org/10.11591/ijece.v7i4.pp1973-1982.
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<span>Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. Previous researches implemented ANPR system on personal computer (PC) with high resolution camera and high computational capability. On the other hand, not many researches have been conducted on the design and implementation of ANPR in smartphone platforms which has limited camera resolution and processing speed. In this paper, various steps to optimize ANPR, including pre-processing, segmentation, and optical character recognition (OCR) using artificial neural network (ANN) and template matching, were described. The proposed ANPR algorithm was based on Tesseract and Leptonica libraries. For comparison purpose, the template matching based OCR will be compared to ANN based OCR. Performance of the proposed algorithm was evaluated on the developed Malaysian number plates’ image database captured by smartphone’s camera. Results showed that the accuracy and processing time of the proposed algorithm using template matching was 97.5% and 1.13 seconds, respectively. On the other hand, the traditional algorithm using template matching only obtained 83.7% recognition rate with 0.98 second processing time. It shows that our proposed ANPR algorithm improved the recognition rate with negligible additional processing time.</span>
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Gunawan, Teddy Surya, Abdul Mutholib, and Mira Kartiwi. "Design of Automatic Number Plate Recognition on Android Smartphone Platform." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (January 1, 2017): 99. http://dx.doi.org/10.11591/ijeecs.v5.i1.pp99-108.
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<p>Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. It is a combination of hardware and software designed to offer the optimum reliability. Since the past decades, many researchers have been proposed to recognize the vehicle number plate and implemented it in various access control, law enforcement and security, including parking management system, toll gate access, border access, tracking of stolen vehicles and traffic violations (speed trap, illegal parking, etc). However, previous researches implemented ANPR system on personal computer (PC) with high resolution camera and high computational capability. On the other hand, not many researches have been conducted on the design of ANPR in Android smartphone platform which has limited camera resolution and limited computational power. The main challenges of implementation ANPR algorithm on smartphone are higher coding efficiency, lower computational complexity, and higher the scalability. The objectives of this research is to design ANPR on Android smartphone, including graphical user interface (GUI) design, process design, and database design. First, a comprehensive survey on the pre-processing, segmentation, and optical character recognition is conducted. Secondly, proposed system development and algorithm implementation is explained in more details. Results show that our proposed design can be implemented effectively in Android smartphone platform.</p>
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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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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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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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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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A. G, S. Fakhar, M. Saad H, A. Fauzan K, R. Affendi H., and M. Aidil A. "Development of portable automatic number plate recognition (ANPR) system on Raspberry Pi." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (June 1, 2019): 1805. http://dx.doi.org/10.11591/ijece.v9i3.pp1805-1813.
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ANPR system is used in automating access control and security such as identifying stolen cars in real time by installing it to police patrol cars, and detecting vehicles that are overspeeding on highways. However, this technology is still relatively expensive; in November 2014, the Royal Malaysian Police (PDRM) purchased and installed 20 units of ANPR systems in their patrol vehicles costing nearly RM 30 million. In this paper a cheaper alternative of a portable ANPR system running on a Raspberry Pi with OpenCV library is presented. Once the camera captures an image, image desaturation, filtering, segmentation and character recognition is all done on the Raspberry Pi before the extracted number plate is displayed on the LCD and saved to a database. The main challenges in a portable application include crucial need of an efficient code and reduced computational complexity while offering improved flexibility. The performance time is also presented, where the whole process is run with a noticeable 3 seconds delay in getting the final output.
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Salma, Maham Saeed, Rauf ur Rahim, Muhammad Gufran Khan, Adil Zulfiqar, and Muhammad Tahir Bhatti. "Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR." Complexity 2021 (October 19, 2021): 1–14. http://dx.doi.org/10.1155/2021/5597337.
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The metropolis of the future demands an efficient Automatic Number Plate Recognition (ANPR) system. Since every region has a distinct number plate format and style, an unconstrained ANPR system is still not available. There is not much work done on Pakistani number plates because of the unavailability of the data and heterogeneous plate formations. Addressing this issue, we have collected a Pakistani vehicle dataset having various plate configurations and developed a novel ANPR framework using the dataset. The proposed framework localizes the number plate region using the YOLO (You Only Look Once) object detection model, applies robust preprocessing techniques on the extracted plate region, and finally recognizes the plate label using OCR (optical character recognition) Tesseract. The obtained mAP score of the YOLOv3 is 94.3% and the YOLOv4 model is 99.5% on the 0.50 threshold, whereas the average accuracy score of our framework is found to be 73%. For comparison and validation, we implemented a LeNet Convolutional Neural Network (CNN) architecture which uses the segmented image as an input. The comparative analysis shows that the proposed ANPR framework comprising the YOLOv4 and OCR Tesseract has good accuracy and inference time for a wide variation of illumination and style of Pakistani number plates and can be used to develop a real-time system. The proposed ANPR framework will be helpful for researchers developing ANPR for countries having similar challenging vehicle number plate formats and styles.
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Kaimkhani, Naveed Ali Khan, Muhammad Noman, Sabit Rahim, and Hannan Bin Liaqat. "UAV with Vision to Recognise Vehicle Number Plates." Mobile Information Systems 2022 (October 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/7655833.
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The trend of using Unmanned Aerial Vehicles (UAVs) in industries is rapidly growing. Nowadays, they are used in many applications, from surveillance to disaster assistance. Almost all applications of UAVs require cameras either to perform specific vision tasks such as facial recognition and vehicle number plate identification or to avoid obstacles in the flight path of the UAV. The most emerging application of UAVs today is to provide security and surveillance, and they are mostly used for vehicle number identification. Typical Automatic Number Plate Recognition (ANPR) uses static high-resolution cameras mounted in specific places to identify the vehicle’s number plates. Identifying the characters on the number plate becomes a very crucial task when the number plate is at an arbitrary angle to the drone camera. The camera gimble angle, the height of the UAV from the vehicle, and the relative speed of the UAV w.r.t vehicle play a very key role in identifying the vehicle license plate correctly. This study explains how Automatic Number Plate Recognition (ANPR) is performed on real-time images using MATLAB with the help of a UAV to analyze the effect of the above-mentioned key factors. The process is completed in three steps: collecting visual data from the drone, processing that data, and obtaining the recognized number plate.
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Chen, Yixian, and Zhaocheng He. "Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding." Sustainability 12, no. 8 (April 11, 2020): 3074. http://dx.doi.org/10.3390/su12083074.
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Automatic number plate recognition (ANPR) systems, which have been widely equipped in many cities, produce numerous travel data for intelligent and sustainable transportation. ANPR data operate at an individual level and carry the unique identities of vehicles, which can support personalized traffic planning. However, these systems also suffer from the common problem of missing data. Different from the traditional missing cases, we focus on the problem of the loss of vehicle identities in ANPR records due to recognition failure or other environmental factors. To address the issue, we propose a heterogeneous graph embedding framework that constructs a travel heterogeneous information network (THIN) and learns the embeddings of the entities to find the best matched vehicles for the unknown records. As a result, the recovery of vehicle identities is cast as an entity alignment task on a THIN. The proposed method integrates the vehicle group entities and context relations into the THIN for capturing the spatiotemporal relationships in vehicle travel and adopts a holographic embeddings model for better fitting the network structure. Empirically, we test it with a real ANPR dataset collected from Xuancheng, China, which has a densely-distributed camera network. The experiments demonstrate the effectiveness of the proposed graph structure under different missing rates. Further, we compare it with other embedding methods and the results support the superiority of holographic embeddings.
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Gani S. F., Abd, Miskon M. F, Hamzah R. A, Mohamood N, Manap Z, Zulkifli M. F, and Md Ali Shah M. A. S. "A Live-Video Automatic Number Plate Recognition (ANPR) System Using Convolutional Neural Network (CNN) with Data Labelling on an Android Smartphone." International Journal of Emerging Technology and Advanced Engineering 11, no. 10 (October 15, 2021): 88–95. http://dx.doi.org/10.46338/ijetae1021_11.
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Automatic Number Plate Recognition (ANPR) combines electronic hardware and complex computer vision software algorithms to recognize the characters on vehicle license plate numbers. Many researchers have proposed and implemented ANPR for various applications such as law enforcement and security, access control, border access, tracking stolen vehicles, tracking traffic violations, and parking management system. This paper discusses a live-video ANPR system using CNN developed on an Android smartphone embedded with a camera with limited resolution and limited processing power based on Malaysian license plate standards. In terms of system performance, in an ideal outdoor environment with good lighting and direct or slightly skewed camera angle, the recognition works perfectly with a computational time of 0.635 seconds. However, this performance is affected by poor lighting, extremely skewed angle of license plates, and fast vehicle movement.
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Paruchuri, Harish. "Application of Artificial Neural Network to ANPR: An Overview." ABC Journal of Advanced Research 4, no. 2 (December 31, 2015): 143–52. http://dx.doi.org/10.18034/abcjar.v4i2.549.
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Vehicle owner documentation and traffic flow mechanism have contributed to a major issue in each country. From time to time it turns out to be challenging to detect car owners who fault traffic regulations. Hence, it of interest to us to investigate designs for automatic number plate detection structure as a clarification and proffer solution to this issue. There are several automatic number plate detection or recognition structure existing today. The structure is according to diverse methods nonetheless automatic number plate recognition is still a difficult job as many of the parameters such as a fast-moving vehicle, non-uniform car number plate, the language used in writing the vehicle number and various lighting situations may hinder 100% detection rate. Many of the structure-function underneath these boundaries. This paper review diverse methods of automatic number plate recognition considering success rate, picture size, and processing time as factors. However, automatic number plate detection is recommended for traffic regulating agencies.
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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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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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Ahmed, Ahmed Abdelmoamen, and Sheikh Ahmed. "A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition." Algorithms 14, no. 11 (October 30, 2021): 317. http://dx.doi.org/10.3390/a14110317.
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Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology’s importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate’s location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters’ contours within the grayscale image. Third, the detected the alphanumeric characters’ contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.
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Druki, A. A., J. A. Bolotova, and V. G. Spitsyn. "Application of Convolutional Neural Networks for Automatic Number Plate Recognition on Complex Background Images." Applied Mechanics and Materials 756 (April 2015): 695–703. http://dx.doi.org/10.4028/www.scientific.net/amm.756.695.
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The relevance of this study is stipulated by the necessity of designing techniques, algorithms, and programs improving the efficiency of automatic number plate recognition (ANPR) on images with complex backgrounds.Purpose: The aim of this work is to improve the efficiency of automatic number plate recognition on images with complex backgrounds using methods, algorithms, and programs invariant to affine and projective transformations.Design/methodology: Such techniques as artificial intelligence, pattern identification and recognition, the theory of artificial neural networks (ANN), convolutional neural networks (CNN), evolutionary algorithms, mathematical modeling, the probability theory and mathematical statistics were applied via Visual Studio and MatLab software.Findings: The software is developed allowing the automatic number plate recognition on complex background images. The convolutional neural network comprising seven layers is suggested to identify the plate localization, i.e. finding and isolating the plate on the picture. The pixel intensity histogram-based algorithm was used for character segmentation or finding individual characters on the plates. The convolutional neural network comprising six layers is designed to recognize characters. The suggested software system allows automatic number plate recognition at large angles of inclinations and rather a high speed.
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Prahara, Adhi, Andri Pranolo, and Rafał Dreżewski. "GPU Accelerated Number Plate Localization in Crowded Situation." International Journal of Advances in Intelligent Informatics 1, no. 3 (November 1, 2015): 150. http://dx.doi.org/10.26555/ijain.v1i3.46.
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Number Plate Localization (NPL) has been widely used as part of Automatic Number Plate Recognition (ANPR) system. NPL method determines the accuracy of ANPR system. Although it is a mature research, the challenge stills persist especially in crowded situation where many vehicles present. Therefore, a method is proposed to localize number plate in crowded situation. The proposed NPL method uses vertical edge density to extract potential region of number plate then detect the number plate using combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). The method employs GPU to deal with multiple number plate detection, to handle multi-scale detection window, and to perform real time detection. The test result shows good results, 0.9883 value of AUC (Area Under Curve), and 0.9362 of BAC (Balance Accuracy). Moreover, potential real time detection is foreseen because total process is executed in less than 50 ms. Errors are mainly caused by background that contain letters, non-standard number plate and highly covered number plate
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Krishnakumar, Priyamvadha. "Automatic Number Plate Recognition (ANPR) through smart Phones using Image Processing Techniques." IOSR journal of VLSI and Signal Processing 4, no. 4 (2014): 19–23. http://dx.doi.org/10.9790/4200-04431923.
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Thombre, Prof Supriya. "Automatic Number Plate Recognition (ANPR) With E-challan using Super Resolution Algorithm." Bioscience Biotechnology Research Communications 13, no. 14 (December 25, 2020): 231–34. http://dx.doi.org/10.21786/bbrc/13.14/54.
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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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Ozbaran, Yavuz, and Serkan Tasgin. "Using cameras of automatic number plate recognition system for seat belt enforcement a case study of Sanliurfa (Turkey)." Policing: An International Journal 42, no. 4 (August 12, 2019): 688–700. http://dx.doi.org/10.1108/pijpsm-07-2018-0093.
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Purpose The purpose of this paper is to quantify the effect of the enforcement, which was carried out with ANPRs, on seat belt use. Though the Seat belt Act was enacted in 1992, it did not lead to an expected increase in seat belt use in Turkey including Sanliurfa, which is one of the immense provinces with a population of over 2m. The Sanliurfa Police Department set in an enforcement campaign, in which automatic number plate recognition (ANPR) cameras were used to facilitate an increment in using seat belts in the city center. Under the police leadership, seat belt use enforcement campaign was hugely publicized and sustained throughout the city. Design/methodology/approach The ANPRs did not have a feature to detect seat belt wearing automatically. Thus, this study tested whether automated plate recognition cameras have a deterrence effect on seat belt usage. To assess the efficacy of this enforcement project, the authors employed a pre/post-implementation design. For this study, the records of the 11 ANPR camera sites, 2 non-camera sites and 2 control sites were utilized. Findings The results of this study revealed that the seat belt use rate was around 8 percent, before camera enforcement in Sanliurfa. Overall increases were 12 percent during the warning period, 60 percent for the beginning period and 78 percent three months after enforcement began at camera sites. One-way ANOVA results suggested the differences between means of seat belt use counts were statistically significant F (3, 61,596)=15,456, p=0.000. Research limitations/implications The findings suggest that there are several reasons for the substantial increase in the seat belt use rate. The first reason for the success of the cameras was their deterrent effect on the drivers, because the drivers were aware that the traffic offense had become readily observable via camera detection in the intersections, and the drivers did not want to be penalized. Second, it is considered that a well-organized publicity of the cameras made a significant contribution to the effectiveness of the enforcement by increasing perceived detection risk. Finally, it is considered that the reason behind the sudden increase in seat belt use was the red-light cameras that had been already in use in Sanliurfa. Namely, the experience of the drivers about camera enforcement gave rise to the rapid decrease in seat belt violation rate in the warning period. Practical implications Using cameras (automatic or not) for seat belt enforcement and publicizing this enforcement can help to save resources and lives. Originality/value This study found a lot of news about similar enforcement on the internet, but no study was found in the literature that reveals if the enforcement can produce an effective result. Thus, this is the first study in Turkey, may be in the world, that evaluated if cameras of the ANPRS can generate effective seat belt enforcement. Furthermore, the study betokened that traffic violations, which cannot be automatically detected by cameras such as cell phone use and smoking in a vehicle can be effectively enforced by non-automatic cameras. Therefore, we believe that the study will contribute policing and the traffic safety literature.
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Sonavane, Kiran, Badal Soni, and Umakanta Majhi. "Survey on Automatic Number Plate Recognition (ANR)." International Journal of Computer Applications 125, no. 6 (September 17, 2015): 1–4. http://dx.doi.org/10.5120/ijca2015905920.
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N, Mahesh Kumar, and Purushotham U. "Smart Parking System using Automatic Number Plate Recognition (ANPR) and the Internet of Things (IoT)." Journal of Image Processing and Artificial Intelligence 7, no. 1 (March 10, 2021): 38–44. http://dx.doi.org/10.46610/joipai.2021.v07i01.005.
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Vidmar, Marko, Marino Žagar, and Mile Perić. "Projection of the Electronic Toll Collection System in the Republic of Croatia." Journal of Maritime & Transportation Science 59, no. 1 (December 2020): 115–30. http://dx.doi.org/10.18048/2020.59.07.
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This paper deals with the topic of a modern electronic toll collection system that will be applied in the Republic of Croatia from the year 2022 onwards. The paper primarily analyses the existing toll collection systems in Croatia, as well as in the European Union. Modern electronic toll collection systems were analysed with an emphasis on the ANPR (Automatic Number Plate Recognition) system, because ANPR technology will be used in Croatia after the restructuring of road traffic occurs. ANPR is not a new technology, however in the last twenty years it has found its wider application. This happened mostly thanks to local and global infrastructural development and technological improvements therefore in turn infrastructure required for the operation of this system became cheaper. By applying the ETC and ANPR, Croatia will have a system in line with European directives and practices which are being applied in other European countries. The system will in turn significantly raise the quality of road traffic in Croatia and reduce its costs.
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Tang, Junqing, Li Wan, Jennifer Schooling, Pengjun Zhao, Jun Chen, and Shufen Wei. "Automatic number plate recognition (ANPR) in smart cities: A systematic review on technological advancements and application cases." Cities 129 (October 2022): 103833. http://dx.doi.org/10.1016/j.cities.2022.103833.
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Sánchez-Cambronero, Santos, Fernando Álvarez-Bazo, Ana Rivas, and Inmaculada Gallego. "Dynamic Route Flow Estimation in Road Networks Using Data from Automatic Number of Plate Recognition Sensors." Sustainability 13, no. 8 (April 15, 2021): 4430. http://dx.doi.org/10.3390/su13084430.
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The traffic flow on road networks is dynamic in nature. Hence, a model for dynamic traffic flow estimation should be a very useful tool for administrations to make decisions aimed at better management of traffic. In fact, these decisions may in turn improve people’s quality of life and help to implement good sustainable policies to reduce the external transportation costs (congestion, accidents, travel time, etc.). Therefore, this paper deals with the problem of estimating dynamic traffic flows in road networks by proposing a model which is continuous in the time variable and that assumes the first-in-first-out (FIFO) hypothesis. In addition, the data used as model inputs come from Automatic Number of Plate Recognition (ANPR) sensors. This powerful data permits not only to directly reconstruct the route followed by each registered vehicle but also to evaluate its travel time, which in turn is also used for the flow estimation. In addition, the fundamental variable of the model is the route flow, which is a great advantage since the rest of the flows can be obtained using the conservation laws. A synthetic network is used to illustrate the proposed method, and then it is applied to the well-known Nguyen-Dupuis and Eastern Massachusetts networks to prove its usefulness and feasibility. The results on all the tested networks are very positive and the estimated flows reproduce the simulated real flows fairly well.
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Davoodi, M., and M. S. Mesgari. "GIS-BASED ROUTE FINDING USING ANT COLONY OPTIMIZATION AND URBAN TRAFFIC DATA FROM DIFFERENT SOURCES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 10, 2015): 129–33. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-129-2015.
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Nowadays traffic data is obtained from multiple sources including GPS, Video Vehicle Detectors (VVD), Automatic Number Plate Recognition (ANPR), Floating Car Data (FCD), VANETs, etc. All such data can be used for route finding. This paper proposes a model for finding the optimum route based on the integration of traffic data from different sources. Ant Colony Optimization is applied in this paper because the concept of this method (movement of ants in a network) is similar to urban road network and movements of cars. The results indicate that this model is capable of incorporating data from different sources, which may even be inconsistent.
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Rakshitha, R., and M. J. Sudhamani. "Virtual Traffic Police." International Journal of Research in Engineering, Science and Management 3, no. 9 (September 20, 2020): 94–99. http://dx.doi.org/10.47607/ijresm.2020.296.
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In the new advancing world, traffic rule infringement has become a focal issue for larger part of the creating nations. The quantities of vehicles are expanding quickly just as the quantities of traffic rule infringement are expanding exponentially. Overseeing traffic rule infringement has consistently been a lethal and trading off undertaking. Despite the fact that the procedure of traffic the executives has gotten robotized, it's an extremely testing issue, because of the decent variety of plate designs, various scales, revolutions and non-uniform brightening conditions during picture obtaining. The vital target of this undertaking is to control the traffic rule infringement precisely and cost adequately. The undertaking presents Automatic Number Plate Recognition (ANPR) strategies and other picture control methods for plate confinement and character acknowledgment which makes it quicker and simpler to recognize the number plates. In the wake of perceiving the vehicle number from number plate the SMS based module is utilized to advise the vehicle proprietors about their traffic rule infringement. An extra SMS is sent to Regional Transport Office (RTO) for following the report status and furthermore to the proprietor of vehicles to advise about the standard infringement.
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Zhang, Yuhui, Yanjie Ji, and Jiajie Yu. "Estimation Method for Road Link Travel Time Considering the Heterogeneity of Driving Styles." Applied Sciences 12, no. 10 (May 16, 2022): 5017. http://dx.doi.org/10.3390/app12105017.
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To solve the problem of low automatic number plate recognition (ANPR) data integrity and low completion accuracy of incomplete traffic data, which affects the quality and utilization of ANPR data, this paper proposed a model for estimating the travel time of the road link that considers the heterogeneity of the driving styles. The travel time of historical road sections in the road network was extracted from ANPR data. The driving crowd was clustered through density-based spatial clustering of applications with noise (DBSCAN) based on the time slot, the number of trips, and the travel time. To avoid the excessive data difference between different classes and the distortion of the complement data, the Lagrange interpolation method was adopted to complement the missing road link travel time within each cluster. Taking Ningbo city in China as an example, the travel time completion accuracies of the proposed method and the direct interpolation method were compared. The results show that the interpolation method considering the heterogeneity of driving styles is more sufficient to increase the completion accuracy by 37.4% compared with the direct interpolation manner. The comparison result verifies the effectiveness of the proposed method and can provide more reliable data support for the construction of the transportation system.
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Dingli, Alexiei, and Maria Attard. "VaTIS." International Journal of Conceptual Structures and Smart Applications 4, no. 2 (July 2016): 1–15. http://dx.doi.org/10.4018/ijcssa.2016070101.
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This paper highlights the concepts behind the Valletta Travel Information Service (VaTIS) Project, an intelligent information system capable of harvesting data from a road pricing system using automatic number plate recognition (ANPR) technology and low cost sensors installed throughout the city of Valletta, Malta. In this paper, we describe the different elements of VaTIS and report on the first phase of the system, which has already been implemented using data from the camera system in place in Valletta and secondary data on travel behaviour to the city. Both authors act as observers, having been involved in the design and subsequent operational procedures of the road pricing system. The initial evaluation of phase 1 shows the potential benefits of smart applications and sensors for the management of travel demand and effective use of limited infrastructure and provides opportunity for further development of such applications to include user engagement and potential behaviour change.
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Du, Wenjun, Bo Sun, Jiating Kuai, Jiemin Xie, Jie Yu, and Tuo Sun. "Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion." Journal of Advanced Transportation 2021 (July 9, 2021): 1–16. http://dx.doi.org/10.1155/2021/9512501.
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Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).
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Chen, Li, Ian Grimstead, Daniel Bell, Joni Karanka, Laura Dimond, Philip James, Luke Smith, and Alistair Edwardes. "Estimating Vehicle and Pedestrian Activity from Town and City Traffic Cameras." Sensors 21, no. 13 (July 3, 2021): 4564. http://dx.doi.org/10.3390/s21134564.
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Traffic cameras are a widely available source of open data that offer tremendous value to public authorities by providing real-time statistics to understand and monitor the activity levels of local populations and their responses to policy interventions such as those seen during the COrona VIrus Disease 2019 (COVID-19) pandemic. This paper presents an end-to-end solution based on the Google Cloud Platform with scalable processing capability to deal with large volumes of traffic camera data across the UK in a cost-efficient manner. It describes a deep learning pipeline to detect pedestrians and vehicles and to generate mobility statistics from these. It includes novel methods for data cleaning and post-processing using a Structure SImilarity Measure (SSIM)-based static mask that improves reliability and accuracy in classifying people and vehicles from traffic camera images. The solution resulted in statistics describing trends in the ‘busyness’ of various towns and cities in the UK. We validated time series against Automatic Number Plate Recognition (ANPR) cameras across North East England, showing a close correlation between our statistical output and the ANPR source. Trends were also favorably compared against traffic flow statistics from the UK’s Department of Transport. The results of this work have been adopted as an experimental faster indicator of the impact of COVID-19 on the UK economy and society by the Office for National Statistics (ONS).
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Borowska-Stefańska, Marta, Michał Kowalski, Paulina Kurzyk, Miroslava Mikušová, and Szymon Wiśniewski. "Application of Intelligent Transportation Systems in Analyses of Human Spatial Mobility in Cities." Prace Komisji Geografii Komunikacji PTG 24, no. 1 (2021): 7–30. http://dx.doi.org/10.4467/2543859xpkg.21.001.14944.
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This article contains results of studies on the applicability of data from Intelligent Transportation Systems (ITS) for the purposes of geographical studies regarding the spatial mobility of inhabitants within a big city. The article focuses on the option of applying two types of sub-systems – induction loops and automatic number-plate recognition (ANPR) – and includes examples of analyses based on the resulting data, which can serve as a basis for mobility studies. The area on the example of which the capabilities of application of ITS data have been presented is Lodz – a large city in central Poland. The conducted research shows that ITS systems offer an enormous potential in providing data for spatial mobility studies. In order to fully exploit its worth, however, it is imperative to expand the research procedure by including, for instance, the results of qualitative research. Also, the interpretation of results obtained on the basis of ITS data ought to be performed with an awareness of numerous significant preliminary and simplifying assumptions.
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Herdiana, Yudi. "KENYAMANAN DAN KEMANAN PADA SISTEM PARKIR OTOMOTIS BERBASIS SENSOR." TEMATIK 6, no. 1 (June 29, 2018): 39–53. http://dx.doi.org/10.38204/tematik.v6i1.186.
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Jumlah kendaraan meningkat dari hari ke hari secara cepat, hal ini menyebabkan masalah lalu lintas kemacetan, polusi (kebisingan dan udara). Untuk mengatasi masalah ini diperlukan sebuah sistem parkir otomatis berbasis sensor. Sistem Sensor menggunakan antarmuka seperti sensor interfacing, motor stepper dan LCD. Beberapa sistem parkir ada yang melibatkan berbagai teknik sistem parkir otomatis yang dirancang menggunakan teknologi seperti Automatic Number Plate Recognition-ANPR, RFID, RPI, jaringan sensor nirkabel dan sistem pemesanan ruang otomatis berbasis Wi-Fi tetapi menghadapi masalah-masalah tertentu dalam hal daya, biaya , efisiensi, kecepatan dan faktor eksternal lainnya. Tujuan utamanya adalah untuk mengatasi masalah di atas sehingga sistem ini dapat bekerja lebih cepat, akurat dengan tenaga manusia yang lebih sedikit, kenyamanan pengguna dan dengan biaya pemeliharaan yang lebih sedikit diperlukan. Sistem dengan biaya tenaga manusia lebih sedikit dan menjadi lebih akurat karena semuanya ditangani oleh sistem. Dalam tulisan ini, menyajikan desain dan pengembangan sistem parkir cerdas menggunakan teknologi berdasarkan pada jaringan sensor nirkabel (WSN). Sistem kami menggunakan jaringan sensor nirkabel yang beradaptasi dengan semua jenis parkir mobil yang ada di kota, dan menawarkan pengelolaan konsumsi energi yang lebih baik selama komunikasi nirkabel untuk meningkatkan seumur hidup node sensor dan umur panjang dari WSN
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Long, Zhe, Zuping Zhang, Jinjin Chen, Faiza Riaz Khawaja, and Shaolong Li. "Unlicensed Taxi Detection Model Based on Graph Embedding." Electronics 11, no. 20 (October 20, 2022): 3410. http://dx.doi.org/10.3390/electronics11203410.
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It is widely considered that unlicensed taxis pose a risk to public safety and interfere with the effective management of traffic. Significant human and material resources are expended by traffic control departments to locate these vehicles with limited success. This study suggests a smart, trajectory big data-based approach entitled Trajectory Graph Embedding-based Unlicensed Taxi Detection (TGE-UTD) to identify suspected unlicensed taxis and address this issue. The model implementation comprises three stages: first, the Automatic Number Plate Recognition (ANPR) data are transformed into a trajectory graph; second, a biased random walk is deployed to embed the trajectory graph; and finally, the set of vehicles similar to the known licensed taxis is obtained as the set of suspected unlicensed taxis using the cosine similarity of the vehicle embedding vector. Through precision evaluation and dimension reduction experiments, the performance of the walk model TGE-UTD is compared to that of the no-walk models Word2Vec and Doc2Vec in detecting large vehicles and taxis. TGE-UTD is observed to exhibit the best performance among the three models. This study pioneers the application of machine learning for feature extraction in detecting unlicensed taxis. The model proposed in the study can be deployed to detect unlicensed taxis; moreover, its application can be extended to detect other types of vehicles, providing traffic management departments with supporting vehicle detection information.
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Zheng, Fangfang, Xiaobo Liu, Henk van Zuylen, Jie Li, and Chao Lu. "Travel Time Reliability for Urban Networks: Modelling and Empirics." Journal of Advanced Transportation 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9147356.
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The importance of travel time reliability in traffic management, control, and network design has received a lot of attention in the past decade. In this paper, a network travel time distribution model based on the Johnson curve system is proposed. The model is applied to field travel time data collected by Automated Number Plate Recognition (ANPR) cameras. We further investigate the network-level travel time reliability by connecting the network reliability measures such as the weighted standard deviation of travel time rate and the weighted skewness of travel time rate distributions with network traffic characteristics (e.g., the network density). The weighting is done with respect to the number of signalized intersections on a trip. A clear linear relation between the weighted average travel time rate and the weighted standard deviation of travel time rate can be observed for different time periods with time-varying demand. Furthermore, both the weighted average travel time rate and the weighted standard deviation of travel time rate increase monotonically with network density. The empirical findings of the relation between network travel time reliability and network traffic characteristics can be possibly applied to assess traffic management and control measures to improve network travel time reliability.
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Afandizadeh Zargari, Shahriar, Amirmasoud Memarnejad, and Hamid Mirzahossein. "Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning." Sensors 21, no. 21 (October 26, 2021): 7080. http://dx.doi.org/10.3390/s21217080.
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Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation systems (ITSs). This shift is partly due to the high cost of conducting traditional surveys and partly due to the diversity of scattered data produced by ITSs and the opportunity to derive extra benefits out of this big data. This study attempts to predict the OD matrix of Tehran metropolis using a set of ITS data, including the data extracted from automatic number plate recognition (ANPR) cameras, smart fare cards, loop detectors at intersections, global positioning systems (GPS) of navigation software, socio-economic and demographic characteristics as well as land-use features of zones. For this purpose, five models based on machine learning (ML) techniques are developed for training and test. In evaluating the performance of the models, the statistical methods show that the convolutional neural network (CNN) leads to the best performance in terms of accuracy in predicting the OD matrix and has the lowest error in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the predicted OD matrix was structurally compared with the ground truth matrix, and the CNN model also shows the highest structural similarity with the ground truth OD matrix in the presented case.
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Peppa, M. V., D. Bell, T. Komar, and W. Xiao. "URBAN TRAFFIC FLOW ANALYSIS BASED ON DEEP LEARNING CAR DETECTION FROM CCTV IMAGE SERIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 499–506. http://dx.doi.org/10.5194/isprs-archives-xlii-4-499-2018.
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<p><strong>Abstract.</strong> Traffic flow analysis is fundamental for urban planning and management of road traffic infrastructure. Automatic number plate recognition (ANPR) systems are conventional methods for vehicle detection and travel times estimation. However, such systems are specifically focused on car plates, providing a limited extent of road users. The advance of open-source deep learning convolutional neural networks (CNN) in combination with freely-available closed-circuit television (CCTV) datasets have offered the opportunities for detection and classification of various road users. The research, presented here, aims to analyse traffic flow patterns through fine-tuning pre-trained CNN models on domain-specific low quality imagery, as captured in various weather conditions and seasons of the year 2018. Such imagery is collected from the North East Combined Authority (NECA) Travel and Transport Data, Newcastle upon Tyne, UK. Results show that the fine-tuned MobileNet model with 98.2<span class="thinspace"></span>% precision, 58.5<span class="thinspace"></span>% recall and 73.4<span class="thinspace"></span>% harmonic mean could potentially be used for a real time traffic monitoring application with big data, due to its fast performance. Compared to MobileNet, the fine-tuned Faster region proposal R-CNN model, providing a better harmonic mean (80.4<span class="thinspace"></span>%), recall (68.8<span class="thinspace"></span>%) and more accurate estimations of car units, could be used for traffic analysis applications that demand higher accuracy than speed. This research ultimately exploits machine learning alogrithms for a wider understanding of traffic congestion and disruption under social events and extreme weather conditions.</p>
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Masdiyasa, I. Gede Susrama, Sulianto Bhirawa, and Slamet Winardi. "IDENTIFIKASI PLAT NOMOR KENDARAAN BERMOTOR MENGGUNAKAN METODE MULTI-STEP IMAGE PROCESSING BERBASIS ANDROID." e-NARODROID 5, no. 1 (June 25, 2019): 17–25. http://dx.doi.org/10.31090/narodroid.v5i1.862.
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Sistem plat nomor di Indonesia merupakan warisan dari Kolonial Hindia Belanda yang membagi berdasarkan wilayah karesidenan. Setelah berjalan sekian lama acuan tersebut masih digunakan hingga sekarang yang terdiri dari dua baris, yaitu baris pertama terdiri dari Kode Wilayah Karesidenan diikuti nomor polisi dari jenis kendaraan bermotor tersebut dan terakhir adalah kode wilayah dari karesidenan tersebut, baris kedua merupakan masa berlaku plat nomor tersebut yang terdiri dari bulan dan tahun. Plat nomor Kendaraan bermotor di Indonesia menggunakan bahan almunium dengan ketebalan 1mm dan mempunyai dimensi 250×105 mm untuk roda dua atau tiga sedangkan untuk roda empat atau lebih mempunyai dimensi 395×135 mm. Peruntukannya berbeda-beda sesuai dengan warna dasar dari plat nomor tersebut, warna yang ada pada plat nomor adalah hitam, merah, kuning, dan putih. Serta ada logo kepolisian sebagai hak cipta dan pemegang merk plat nomor di Indonesia. Keberadaan plat nomor tersebut memberikan identitas dari setiap kendaraan bermotor yang beredar di Indonesia, sehingga mudah mengenali kendaraan tersebut berasal dari wilayah mana sesuai dengan kode plat nomor yang tertera pada posisi depan dan belakang kendaraan bermotor. Identitas kendaraan tersebut kurang optimal penggunaannya untuk keperluan-keperluan tertentu yang membutuhkan identitas secara cepat dan akurat, sehingga untuk keperluan tersebut diperlukan sebuah ID digital yang dapat digunakan untuk identifikasi kendaraan bermotor secara cepat dan akurat. Salah satu teknologi digitalisasi plat nomor kendaraan yang sudah digunakan di beberapa negara yaitu ANPR (Automatic Number Plate Recognition ). Teknologi ini menggunakan pengolahan citra digital untuk mengenali dan membaca plat nomor kendaraan. Gambar atau citra yang diolah didapatkan dari kamera pengawas dan juga kamera yang didesain khusus untuk proses pengolahan digital. Mengingat ANPR menggunakan sebuah algoritma dalam pengolahan citra digital, maka memungkinkan untuk mengembangkan perangkat lunak dengan kemampuan serupa namun dalam perangkat yang lebih terjangkau dan murah. Tentu saja juga memungkinkan untuk dikembangkan di perangkat smartphone berbasis sistem operasi Android. Hal ini dikarenakan smartphone memiliki kamera serta kemampuan komputasi yang dibutuhkan untuk pengolahan citra digital. Dengan metode Multi-step image processing ini mampu mengenali plat nomor kendaraan bermotor sampai dengan akurasi 95% dengan kamera minimal 5 megapixel yang dilakukan dalam tiga tahap yaitu threshold, contour dan kNearestNeighbors. Dalam pemindaian jarak menentukan kemampuan untuk mendeteksi segmen plat nomor dan pencahayaan mempengaruhi tingkat akurasi pengenalan karakter sedangkan untuk pengenalan plat nomor yang memiliki dasar warna terang harus dilakukan invers terlebih dahulu.
Kata kunci : plat nomor, pengolahan citra digital, mobile application, android, multi-step image processing
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Gujjula, Karthik Reddy. "Car Number Plate Detection from Livestream." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1555–60. http://dx.doi.org/10.22214/ijraset.2022.44133.
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Abstract- As of late there has been an expansion in the quantity of vehicles and a few methods of transports accessible in the street organizations. Also, consequently need to screen the traffic have become a gigantic issue. Different strategies have been executed on the traffic the executives and its reconnaissance. For this issue the number plate recognition is useful as it is perhaps the main methodologies for managing the traffic utilizing a few advanced procedures. Programmed Number Plate Recognition (ANPR) framework is perhaps the best answer for this issue. In our country regularly number plates with different text styles are being used which add to intricacy of understanding tag as framework should be made for all regions. In this gave work an overview on mechanized. amount plate acknowledgment method is introduced. In this undertaking we do an investigation of the current ANPR frameworks.
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Nayak,, Veena. "Automatic number plate recognition." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 3 (June 25, 2020): 3783–87. http://dx.doi.org/10.30534/ijatcse/2020/195932020.
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Srivastava, Gaurav. "Automatic Number Plate Recognition." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (June 30, 2020): 1105–8. http://dx.doi.org/10.22214/ijraset.2020.6178.
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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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Patil., Vijeeta. "AUTOMATIC VEHICLE NUMBER PLATE RECOGNITION." International Journal of Advanced Research 7, no. 8 (August 31, 2019): 186–217. http://dx.doi.org/10.21474/ijar01/9492.
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Gnanaprakash, V., N. Kanthimathi, and N. Saranya. "Automatic number plate recognition using deep learning." IOP Conference Series: Materials Science and Engineering 1084, no. 1 (March 1, 2021): 012027. http://dx.doi.org/10.1088/1757-899x/1084/1/012027.
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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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NAKAO, Kenta. "Automatic Number Plate Recognition Techniques for ITS." Journal of the Society of Mechanical Engineers 114, no. 1114 (2011): 666–67. http://dx.doi.org/10.1299/jsmemag.114.1114_666.
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