License plate recognition based on SIFT feature

The intent of this research is to design a license plate recognition (LPR) system in the domain of Bangla language for smart vehicle management. The proposed system is designed on the basis of computer vision tools and deep supervised machine learning model. The system has three modules: license plate detection, character segmentation and recognition of the characters of the License Plate (LP). The goal of detection is to localize the plate area from the vehicle image and to crop region of interest (LP). It is executed by applying following process: preprocessing the image, conversion to binary image, contour detection and filtering the contours to get the LP's character contours, tilt correction and cropping the plate area from the image. Then, the cropped LP is segmented to extract the characters from the plate. Finally, the recognition step classifies the characters by means of deep convolution neural network where the features of the character are crafted and learned by the convolution layers of the networks. The system is implemented in Python OpenCV environment for offline car license plates images which are taken in different illuminations, road scenarios and colored cars. The system performance is evaluated in terms of detection rate, segmentation rate, recognition rate and execution time. The results illustrate that the performance of the system is remarkable.

With the increasing advancement of science and technology, the popularity of automobiles in my country has gradually increased. Among them, license plate positioning and license plate recognition technology are very important key technologies in the license plate recognition system. Through the license plate positioning technology, the position information, centroid and other data information of the license plate can be obtained, so as to realize the positioning of the license plate. The license plate recognition technology can extract the uniqueness of the vehicle. Identifying the license plate number, so the design and implementation of vehicle recognition and license plate recognition technology based on embedded devices have practical significance and practical application value to promote the technological progress of intelligent transportation and vehicle recognition systems. This design is a license plate recognition system, an embedded character segmentation recognition processing platform with ARM Cortex-A9 as the core, and the method of cross-compiling OpenCV, Qt, BootLoader in the Linux operating system, and transplanting related drivers to the ARM Cortex-A9 platform; Based on the classic algorithm, the OpenCV library function is used to realize the segmentation and recognition of the license plate image characters. The experimental results show that the design has achieved the expected goal.

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

In this paper, we propose a novel image segmentation algorithm for license plate recognition (LPR) in video based traffic surveillance system. The license plate character segmentation is most important procedure in LPR system. However, in real situation, the character segmentation algorithms are challenged by drastic performance decrease due to sudden local illumination changes, especially when the color of characters is similar to that of background in LP. To mitigate this problem, we introduce a novel LP character segmentation algorithm by employing an adaptive binarization method using super-pixel based degeneracy factor. The proposed method demonstrates a significant improvement over conventional methods.

The license plate recognition technology is an important part of the construction of an intelligent traffic management system. This paper mainly researches the image preprocessing, license plate location, and character segmentation in the license plate recognition system. In the preprocessing part of the image, the edge detection method based on convolutional neural network (CNN) is used for edge detection. In the design of the license plate location, this paper proposes a location method based on a combination of mathematical morphology and statistical jump points. First, the license plate area is initially located using mathematical morphology-related operations and then the location of the license plate is accurately located using statistical jump points. Finally, the plate with tilt is corrected. In the process of character segmentation, the border and delimiter are first removed, then the character vertical projection method and the character boundary are used to segment the character for actually using cases.

On the streets of Taiwan, many roadside tollers are often seen riding motorcycles in one hand, and the other hand holding mobile devices to issue payment notices for cars and motorcycles parked on the roadsides. The work of roadside tollers is very dangerous. First, they must first park their motorcycles next to the roadside cars and motorcycles. They then use their eyes to confirm the license plate number, enter the license plate number into the mobile device, and finally place the bill on the car’s windows or attach the bill to the motorcycles. Our idea is to implement an automated license plate recognition system in mobile devices to increase the efficiency of roadside tollers and reduce their time on the road. Recently, license plate recognition systems have been widely used in various aspects of life, such as parking lot toll systems, access management systems, and traffic management systems. However, existing license plate recognition systems must have good recognition rates under a number of constraints, such as fixed angles and fixed light sources. Moreover, due to the insufficient computing resources of the general mobile device, the application cannot have a good recognition rate in the complex environment or skewed angle in the license plate recognition. Therefore, this paper proposes a lightweight and high-performance multi-angle license plate character recognition model, which reduces the complexity and computational complexity of traditional license plate recognition. This paper also collects a large number of license plate images from different environments, angles and sizes as training data. Finally, we propose an optimized deep learning model to identify the characters on license plates. The experimental results show that the proposed model can recognize the license plate with a tilt of 0~60 degrees, and the overall recall rate is 84.5%. Compared with Tiny-YOLOv2, the computation of the proposed model is reduced by 61% with a little penalty of recall.

Automatic license plate recognition (ALPR) is a process of identification of the license plate from an image taken at any signal stop or parking station. The Identified information or the number plate can be used for keeping a check on signal stops or for monetary settlements at parking lots or toll stations. It can also be used around the world for law and administrative works like checking a vehicle if it is registered or not. It also finds its use in safety supervision system. The ALPR can be used upon various types of pictures like color images, black and white images and etc. ALPR may fail a number of times if the quality of the picture is not up to the mark. So, ALPR depend a lot in the image quality, and hence the challenges in its real-life application. It may be used both indoors and outdoors but it must be able to process the images both quickly and successfully in all sorts of environments, be it daytime or night time, sunny or rainy. Our proposed method mainly has three modules: 1) Detection of license plate 2) Segmentation of Characters 3) Text Box Generation. The number plates sometimes may be unclear for the cameras. It may sometimes have dirt and scratches on it and thus increasing the complexities of the ALPR system. Today nationwide ALPR has been adopted by several law and governmental agencies for its efficiency and usefulness. Public safety agencies are using the ALPR system to gather and store relevant information about people and to keep a check on the vehicular activities. It is becoming a significant tool for the Law Enforcement Agencies.

Operator context scanning to support high segmentation rates for real time license plate recognition

In recent years, license plate recognition system has become a crucial role in the development of smart cities for vehicle management, investigation of stolen vehicles, and traffic monitoring and control. License plate recognition system has three stages, including license plate localization, character segmentation, and character recognition. Although the license plate recognition system has been successfully applied to the environment-controlled smart parking system, it still faces many challenging in the surveillance system such as congested traffic with multiple plates, ambiguous signs and advertisements, tilting plates, as well as obscure images taken in bad weather and nighttime. In this paper, we propose an efficient license plate recognition system that first detects vehicles and then retrieves license plates from vehicles to reduce false positives on plate detection. Then, we apply convolution neural networks to improve the character recognition of blurred and obscure images. The experimental results show the superiority of the performance in both accuracy and performance in comparison with traditional license plate recognition systems.

Automatic License Plate Recognition (ALPR) has been the focus of many\nresearches in the past years. In general, ALPR is divided into the following\nproblems: detection of on-track vehicles, license plates detection, segmention\nof license plate characters and optical character recognition (OCR). Even\nthough commercial solutions are available for controlled acquisition\nconditions, e.g., the entrance of a parking lot, ALPR is still an open problem\nwhen dealing with data acquired from uncontrolled environments, such as roads\nand highways when relying only on imaging sensors. Due to the multiple\norientations and scales of the license plates captured by the camera, a very\nchallenging task of the ALPR is the License Plate Character Segmentation (LPCS)\nstep, which effectiveness is required to be (near) optimal to achieve a high\nrecognition rate by the OCR. To tackle the LPCS problem, this work proposes a\nnovel benchmark composed of a dataset designed to focus specifically on the\ncharacter segmentation step of the ALPR within an evaluation protocol.\nFurthermore, we propose the Jaccard-Centroid coefficient, a new evaluation\nmeasure more suitable than the Jaccard coefficient regarding the location of\nthe bounding box within the ground-truth annotation. The dataset is composed of\n2,000 Brazilian license plates consisting of 14,000 alphanumeric symbols and\ntheir corresponding bounding box annotations. We also present a new\nstraightforward approach to perform LPCS efficiently. Finally, we provide an\nexperimental evaluation for the dataset based on four LPCS approaches and\ndemonstrate the importance of character segmentation for achieving an accurate\nOCR.\n

CIS Multilingual License Plate Detection and Recognition Based on Convolutional and Transformer Neural Networks

Traditional license plate recognition technology mostly uses traditional image processing methods to find out the characteristics of the license plate, and then crop and recognize the characters. The process needs to be modified due to the different environments, scenes and conditions. In recent years, many studies have implemented license plate and character recognition by using deep learning algorithms. Although it has a good recognition accuracy, the calculation speed still cannot reach the level of real-time recognition. This research proposes a real-time license plate recognition system based on YOLOv3, which uses deep learning model to realize the vehicle license plate recognition, lane identification and vehicle trajectory tracking. In this study, a web-based platform is established to present the result of license plate recognition and trajectory, and the streaming roadside video in the campus. In the platform, license plates of driving vehicles can be identified in real-time, and the user can search and track specific vehicle intuitively. In the experiment, the average accuracy of the system performs 84.3% in real-time license plate recognition, and 100% in lane identification. The system can process in 40 FPS, which can meet the level of real-time system. In the future, the system can cooperate with traffic access control in campus or community to improve the efficiency of traffic control.

Automatic license plate recognition (ALPR) systems have become an important tool to track stolen cars, access control, and monitor traffic. ALPR system consists of locating the license plate in an image, followed by character detection and recognition. Since the license plate can exist anywhere within an image, localisation is the most important part of ALPR and requires greater processing time. Most ALPR systems are computationally intensive and require a high‐performance computer. The present algorithm differs significantly from those utilised in previous ALPR technologies by offering a fast algorithm, composed of structural elements which more precisely conducts morphological operations within an image, and can be implemented in portable devices with low computation capabilities. The present algorithm is able to accurately detect and differentiate license plates in complex images. This method was first tested through MATLAB with an on‐line public database of Greek number plates and was 100% accurate in all clear images, and achieved 98.45% accuracy when using the entire database which included complex backgrounds and license plates obscured by shadow and dirt. Second, the efficiency of the algorithm was tested in devices with low computational processing power, by translating the code to Python, and was 300% faster than previous work.

Automatic license plate recognition (ALPR) systems critically depend on the robust and efficient detection of LPs under unconstrained environmental conditions, including significant viewpoint variations and complex backgrounds. To address these challenges, this paper introduces CAD‐Net, a novel corner‐aware LP detection architecture that combines a computationally efficient ResNet‐18 encoder with an efficient multi‐scale feature decoder for accurate LP corner localization. The decoder aggregates and refines features through group dilated convolutions, coordinate attention, and context gated attention, enabling enhanced focus on semantically salient regions while capturing intricate spatial dependencies. The detected LP corner points enable a polygonal region‐of‐interest alignment strategy for geometric rectification of LP features, which is integrated into an end‐to‐end LP recognition framework named CAR‐Net. Comprehensive experiments demonstrate the efficacy of our method. For LP detection, CAD‐Net attains LP detection rates of 99.9% on CCPD‐Base and 100.0% on AOLP‐RP, with a processing speed of 105 frames per second. For end‐to‐end LP recognition, CAR‐Net achieves state‐of‐the‐art performance on multiple benchmarks, CCPD (98.9%), AOLP‐RP (99.2%), PKUdata (98.5%), CLPD (82.3%), and OpenALPR‐BR (99.1%), while maintaining a real‐time inference speed of 72 frames per second. These results confirm practical viability for deployment in real‐world ALPR systems.

License plate localization (LPL) and character segmentation (CS) play key roles in the license plate (LP) recognition system. In this paper, we dedicate ourselves to these two issues. In LPL, histogram equalization is employed to solve the low-contrast and dynamic-range problems; the texture properties, e.g., aspect ratio, and color similarity are used to locate the LP; and the Hough transform is adopted to correct the rotation problem. In CS, the hybrid binarization technique is proposed to effectively segment the characters in the dirt LP. The feedback self-learning procedure is also employed to adjust the parameters in the system. As documented in the experiments, good localization and segmentation results are achieved with the proposed algorithms.