License Plate Character Research Articles

License Plate Recognition Using Three-Dimensional Rotated Character Recognition and Instance Segmentation by Deep Learning

License plate recognition is currently used in various situations, such as parking lot vehicle management and the tracking of wanted vehicles. These applications require recognition from all possible camera angles. Therefore, we propose a method for license plate recognition in images captured from various camera angles. First, the license plate area is detected from the input image using the YOLOv5 object detection method. The subsequent process can be performed in two manners: by rotating the license plate to the front using projective transformation and trapezoidal correction, or by using graph matching. The first method performs Hough transforms or linear approximations as preprocessing, and then calculates the rectangle surrounding the license plate from the YOLOv5 output. After performing trapezoidal correction, the maximally stable extremal regions (MSER) are used to detect character candidates, and characters are recognized using 3D rotated character recognition. The second method performs character candidate detection and recognition without trapezoidal correction, followed by graph matching. These methods are versatile because they do not depend on the layout of license plates, which varies among countries and regions. Two types of datasets were used: a set of images containing Japanese license plates collected by ourselves, and a set of publicly available images containing Taiwanese license plates. Two recognition rates were evaluated: the license plate character recognition rate and license plate recognition rate in which all characters in one plate are recognized. The license plate character recognition rates using graph matching were 91.9% for the Japanese license plates and 89.3% for the Taiwanese license plates. The license plate recognition rates were 84.0% and 84.5%, respectively.

Integrating YOLOv8 and CSPBottleneck based CNN for enhanced license plate character recognition

Integrating YOLOv8 and CSPBottleneck based CNN for enhanced license plate character recognition

LPDi GAN: A License Plate De-Identification Method to Preserve Strong Data Utility.

License plate (LP) information is an important part of personal privacy, which is protected by law. However, in some publicly available transportation datasets, the LP areas in the images have not been processed. Other datasets have applied simple de-identification operations such as blurring and masking. Such crude operations will lead to a reduction in data utility. In this paper, we propose a method of LP de-identification based on a generative adversarial network (LPDi GAN) to transform an original image to a synthetic one with a generated LP. To maintain the original LP attributes, the background features are extracted from the background to generate LPs that are similar to the originals. The LP template and LP style are also fed into the network to obtain synthetic LPs with controllable characters and higher quality. The results show that LPDi GAN can perceive changes in environmental conditions and LP tilt angles, and control the LP characters through the LP templates. The perceptual similarity metric, Learned Perceptual Image Patch Similarity (LPIPS), reaches 0.25 while ensuring the effect of character recognition on de-identified images, demonstrating that LPDi GAN can achieve outstanding de-identification while preserving strong data utility.

Moving Vehicle Number Plate Detection

Abstract: The ability to detect and recognize vehicle license plates is a crucial task in various applications such as traffic management, law enforcement, and parking systems. In this project, we propose a system for moving vehicle license plate detection using image and video processing techniques. The system employs a combination of computer vision algorithms and machine learning models to accurately locate and recognize license plates from moving vehicles in real-time. The input images or video frames are preprocesses to enhance the regions containing license plates and reduce noise. This preprocessing step may include operations such as resizing, grayscale conversion, noise reduction, and contrast enhancement. Next, object detection algorithms such as Haar cascades or deep learning-based techniques like YOLO (You Only Look Once) are utilized to detect candidate regions in the image/frame that potentially contain license plates. After candidate regions are identified, a series of image processing techniques such as morphological operations, contour detection, and character segmentation are employed to isolate and extract the license plate characters. Machine learning models like Support Vector Machines (SVM), Convolutional Neural Networks (CNNs) are then applied to recognize the characters on the license plate.

Disentangled generation network for enlarged license plate recognition and a unified dataset

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above challenges, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. In this work, we address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high labor cost. To handle this problem, we propose a novel data generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation of enlarged license plate data into the text generation and background generation in an end-to-end manner to effectively ensure the generation diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks.

Smart Parking Locks Based on Extended UNET-GWO-SVM Algorithm.

Due to the rapid increase in private car ownership in China, most cities face the problem of insufficient parking spaces, leading to frequent occurrences of parking space conflicts. There is a wide variety of parking locks available on the market. However, most of them lack advanced intelligence and cannot cater to the growing diverse needs of people. The present study attempts to devise a smart parking lock to tackle this issue. Specifically, the smart parking lock uses a Raspberry Pi as the core controller, senses the vehicle with an ultrasonic ranging module, and collects the license plate image with a camera. In addition, algorithms for license plate recognition based on traditional image-processing methods typically require a high pixel resolution, but their recognition accuracy is often low. Therefore, we propose a new algorithm called UNET-GWO-SVM to achieve higher accuracy in embedded systems. Moreover, we developed a WeChat mini program to control the smart parking lock. Field tests were conducted on campus to evaluate the performance of the parking locks. The test results show that the corresponding effective unlocking rate is 99.0% when the recognition error is less than two license plate characters. The average time consumption is controlled at about 2 s. It can meet real-time requirements.

Sistem Pemantauan Slot Parkir Menggunakan Sensor Ultrasonik JSN-SR04T dan Pengenalan Plat Nomor Kendaraan dengan ESP32-CAM

A parking slot monitoring system prototype has been produced using the JSN-SR04T ultrasonic sensor and vehicle license plate recognition with ESP32-CAM. The system was created to make it easier for motorists to monitor the availability of parking slots remotely by utilizing Telegram which is connected to the ESP32-CAM module and Arduino Mega 2560. The ultrasonic sensor detects the distance of vehicles entering or exiting the parking lot, then a servo motor opens the entry and exit bars. Vehicle users can monitor the availability of parking slots on the Telegram bot. In each slot there is an ultrasonic sensor that detects the distance so that it can find out whether there is a vehicle in the slot. The test results show that the ultrasonic sensor has an average error percentage of 0.65%. The parking barrier can be opened when the ultrasonic sensor detects the presence of a vehicle at the entrance or exit and the ultrasonic sensor can detect the presence of a vehicle according to the conditions in the slot. The system is equipped with vehicle license plate character recognition using the Optical Character Recognition (OCR) method. Through the ESP32-CAM module camera webserver, the OCR program can detect vehicle plates with the furthest distance of 140 cm using video resolution (400x296) and 300 cm using video resolution (1600x1200). The OCR program can read the vehicle plate characters correctly and the results for the number of characters obtained correspond to the license plate being tested, then the vehicle plate data is stored in the database

Bounding Box and Thresholding in Optical Character Recognition for Car License Plate Recognition

License plate recognition plays a central role in a variety of application contexts, including traffic management, automated parking, and law enforcement. Among the various approaches available, the Optical Character Recognition (OCR) technique has proven its effectiveness in recognizing characters in license plate images. This study describes an approach for detecting and recognizing vehicle license plates by utilizing the OCR method with Bounding Box, Thresholding, and template matching. In addition, this study uses MATLAB R2022a software as the main tool in developing and implementing the method. The goal is to recognize vehicle license plates from images, describe their characteristics, and generate relevant information. This approach involves a series of image processing steps starting with the pre-processing stage, followed by the process of binarization and license plate segmentation. After successfully isolating the license plate area, isolating the character using a bounding box is performed using image separation techniques. The OCR method is used to recognize license plate characters through comparison using the correlation method. Through a series of experiments on several image datasets, this approach succeeded in showing that out of 20 sampled license plate images, the results obtained were a reading accuracy of 93.55% of 100%, recognizing 13 out of 20 license plate images accurately when tested. Thus, the findings of this research are expected to contribute to the recognition of vehicle license plates that are accurate and efficient, by utilizing image processing techniques and OCR methods implemented using MATLAB R2022a software.

High Precision License Plate Recognition Algorithm in Open Scene

At present, license plate recognition algorithm under restricted conditions is relatively mature and widely used in various license plate recognition system. Due to the influence of factors such as large differences in shooting angles and vehicle motion blur, Chinese license plate recognition is quite challenging. In response to the above problems, this research abandoned the single end-to-end deep learning license plate recognition method, and proposed a step-by-step license plate recognition algorithm that integrated detection and classification, and utilized a level-by-level object detection strategy combined with character classification to predict the characters of the license plate result. On the basis of the above, a multi-anchor character position regression algorithm was proposed to further accurately regress the local area position information of all license plate characters. At the same time, in order to meet the needs of character detection and character classification, as well as the imbalance of the existing license plate datasets, this study contributed a series of supporting license plate datasets. According to the published publications, this study contributed the first large-scale character-level annotated license plate dataset. Extensive experiments show that the method in this study can reach the current state-of-the-art on different datasets. If accepted, the dataset will be publicly available at https://gitee.com/wust30405/lpdataset.

Reproduction of spatial–temporal distribution of traffic loads on freeway bridges via fusion of camera video and ETC data

Understanding the spatial–temporal distribution of traffic loads on bridge decks is essential for managing traffic flow and assessing bridge load effects. While computer vision can already identify on-bridge vehicle parameters through traffic surveillance cameras, obtaining cost-effective vehicle load information remains a challenge, even though it is the most important service load for bridges. In this study, we present a methodology that applies computer vision algorithms to fuzzy match video-identified data with electronic toll collection (ETC) recorded data to reproduce the spatial–temporal distribution of traffic loads on freeway bridges. The proposed method involves three critical steps. First, multi-object detection and trajectory tracking of vehicle contours, vehicle heads, vehicle tires, and vehicle license plates are implemented using YOLOv5 and DeepSort algorithms. Second, the conversion of image coordinates to real-world coordinates is automatically calibrated using pre-measured roadside reflection points. Third, vehicle license plate characters are identified by the LPRnet algorithm and the results are fused with highway ETC records to determine vehicle weight information, while the KNN clustering algorithm is used to further infer vehicle axle weight. After these three steps, the vehicle load sequence in the video surveillance area can be reproduced. Finally, the effectiveness and feasibility of the proposed method are verified in an example of a highway bridge.

A License Plate Recognition System with Robustness against Adverse Environmental Conditions Using Hopfield’s Neural Network

License plates typically have unique color, size, and shape characteristics in each country. This paper presents a general method for character extraction and pattern matching in license plate recognition systems. The proposed method is based on a combination of morphological operations and edge detection techniques, along with the bounding box method for identifying and revealing license plate characters while removing unwanted artifacts such as dust and fog. The mathematical model of foggy images is presented and the sum of gradients of the image, which represents the visibility of the image, is improved. Previous works on license plate recognition have utilized non-intelligent pattern matching techniques. The proposed technique can be applied in a variety of settings, including traffic monitoring, parking management, and law enforcement, among others. The applied algorithm, unlike SOTA-based methods, does not need a huge set of training data and is implemented only by applying standard templates. The main advantages of the proposed algorithm are the lack of a need for a training set, the high speed of the training process, the ability to respond to different standards, the high response speed, and higher accuracy compared to similar tasks.

Chinese License Plate Recognition System Based on Convolutional Neural Network

License Plate Recognition (LPR) technology has been developed for many years, but for the time being, LPR technology still has problems such as not being accurate enough in positioning and too long recognition time. Especially in China, the License Plate (LP) is made up of Chinese characters, alphabets, and numbers, in which the use of Chinese characters profoundly influences the accuracy of LPR. In this paper, a Convolutional Neural Network (CNN) is employed in LPR system, and the LPR system designed in this paper includes three parts: coarse LP positioning, precise LP positioning, and LP character recognition. The LPR system designed in this paper uses the functions in OpenCV to call the laptop camera and input the obtained images into the computer to achieve interframe recognition by setting the timer. Once the image is acquired, the approximate license plate position is determined by the cascade classifier. After this, the image is processed by adaptive binarization, followed by Connected Component Analysis (CCA), linear fitting, and left-right regression to precisely locate the LP. After the exact location of the LP has been determined, it will be fed into a CNN for character recognition. Simulation tests show that the LPR system implemented in this paper has some LPR capability.

A Method for the Fusion of Results from RFID and VLPR

Radio Frequency Identification (RFID) and Vehicle License Plate Recognition (VLPR) are two main methods for vehicle identification. How to fusion the recognition results of the two methods is a problem that needs to be solved in practice. The existing fusion technology usually uses trigger acquisition for precise matching, or uses one recognition result as a reference to perform matching in another result set, while these technology does not provide a clearly algorithm or has certain limitations, and is not universal. A matching algorithm is presented in this paper, which takes advantage of the similarity metric between the results from RFID and VLPR, thus eliminating the drawbacks of existing match methods. The algorithm calculates the similarity metric of the results from RFID and VLPR by the vehicle license plate character graphics similarity, then find matching results from the two recognition results in a specific time window, finally work out precise matching result or similar matching result. The presented algorithm is simple but effective, with no need to triggering RFID and VLPR simultaneously. The algorithm can output both precise matches and similar matches. Through the verification results in the real road scene, the performance in the license plate similarity calculation and fusion matching process is consistent with expectations, which shows the effectiveness of the algorithm.

An efficient automated vehicle license plate recognition system under image processing

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

License Plate Recognition System Based on Artificial Intelligence with Different Approach

An license plate recognition system (LPRS) generally provides control and security. These systems are created using methods such as artificial intelligence, machine learning, artificial neural networks (ANN), deep learning, fuzzy logic, expert systems, and image processing. This study aims to create an LPRS using artificial intelligence and image processing techniques. The prepared system is for rectangular-sized plates. An LPRS consists of 3 main stages. The first stage is to detect the plate region. At this stage, converting to grayscale, bilateral filtering, canny filtering, and contour were applied to vehicle images. The second stage is to crop the plate region. In the second stage, the masking method was employed. The pytesseract algorithm was used to recognize license plate characters in the last stage. To create the system, Raspberry Pi 4 Single-Board Computer (SBC) was used for hardware; python programming language was utilized for software. The results showed that the system worked successfully at the rate of 100% in the first two stages and at the rate of 91.82% in the last stage. The results suggest that the system works successfully.

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

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

A High-Performance Approach for Irregular License Plate Recognition in Unconstrained Scenarios

This paper proposes a novel framework for locating and recognizing irregular license plates in real-world complex scene images. In the proposed framework, an efficient deep convolutional neural network (CNN) structure specially designed for keypoint estimation is first employed to predict the corner points of license plates. Then, based on the predicted corner points, perspective transformation is performed to align the detected license plates. Finally, a lightweight deep CNN structure based on the YOLO detector is designed to predict license plate characters. The character recognition network can predict license plate characters without depending on license plate layouts (i.e., license plates of single-line or double-line text). Experiment results on CCPD and AOLP datasets demonstrate that the proposed method obtains better recognition accuracy compared with previous methods. The proposed model also achieves impressive inference speed and can be deployed in real-time applications.

Research on the Optimization of License Plate Character Segmentation

License plate character segmentation is an important step in license plate detection and recognition. With the development of machine learning technology, the segmentation algorithm of license plate characters based on clustering is also developed rapidly. However, the current clustering algorithm based on K-means does not consider the integrity of the characters and the horizontal difference between the characters on the license plate. This paper presents a weighted distance measurement method based on a connected domain. First, all pixels belonging to the same connected domain are naturally clustered into a class. Meanwhile, the horizontal and vertical distance measurements between the connected domains are scaled by the respective land weights, which makes the horizontal distance between the connected domains get more attention. This paper conducts extensive experiments on the collected data set of license plate characters, and the experimental results verify that compared with the K-means clustering algorithm, the weighted distance measurement method based on the connected domain has more accurate license plate character segmentation results.

Comparison of Vehicle License Plate Detection Algorithms and LP Character Segmentation and Recognition using Image Processing

In the last couple of decades, the number of vehicles has increased drastically, consequently, it is becoming difficult to keep track of each vehicle for purpose of law enforcement and traffic management. License Plate Detection is used increasingly nowadays for the same. The system performing the task of License Plate detection is known as the LPR system which generally consists of three steps: Detection of the License plate, Segmentation of License plate characters, and Recognition of the characters of the License Plate (LP). But in real-world scenarios, the various lighting conditions, camera angle, and rotation degrades the accuracy of License Plate region detection, which in turn causes inaccurate segmentation and recognition of the license plate characters hence leading to low accuracy of the LPR systems. Therefore, it is vital to consider the most promising algorithm or technique for LP detection. In this paper, we will be analyzing and comparing five different methods for license plate detection: Morphological reconstruction, Sobel Operator, Top Hat Transform, Histogram processing, and Canny Edge detection. We will be experimentally applying these techniques on real-time captured vehicle images, using the Bounding Box algorithm for character segmentation, performing license plate character recognition using Template matching, and subsequentially evaluating and demonstrating the LPR system that promises the most accurate and efficient results.

Spatial Transform Depthwise Over-Parameterized Convolution Recurrent Neural Network for License Plate Recognition in Complex Environment

Abstract Automatic license plate recognition (ALPR) system has been widely used in intelligent transportation and other fields. However, in complex environments such as vehicle sound source localization, poor illumination, or bad weather conditions, ALPR is still a challenging problem. Aiming at the problem, an end-to-end deep learning framework is developed based on depthwise over-parameterized convolution recurrent neural network for license plate character recognition. The proposed framework is composed as follows: (i) license plate correcting module based on spatial transformation network; (ii) feature extraction module based on depthwise over-parameterized convolution; (iii) sequence annotation module based on bidirectional long short-term memory; and (iv) regularized sequence decoding module based on connectionist temporal classification with maximum conditional entropy. Two open-source datasets of Chinese License Plate Datasets (SYSU) and Chinese City Parking Dataset (CCPD) are used to verify the performance of the algorithm. The proposed end-to-end framework can effectively rectify distorted and inclined license plates in spatial domain. It can recognize license plates without complex character segmentation process. Compared with some current state-of-art algorithms, the proposed algorithm achieved the best performance with the recognition accuracy of 96.31% and 88.31% based on the two datasets of SYSU and CCPD, respectively.