License Plate Extraction Research Articles

Smart Traffic Management: Enhancing Urban Mobility through Predictive Analysis and AI-Driven Solutions

In urban environments, the issue of unauthorized parking in designated no-parking zones persists, leading to traffic congestion and safety hazards. inaccurate license plate recognition, License plate (LP) detection is a crucial task for Automatic License Plate Recognition (ALPR) systems. Most existing LP detection networks can detect License plates, but their accuracy suffers when license plates (LPs) are tilted or deformed due to perspective distortion. This leading to difficulties in identifying vehicle owners. To address this challenge, this project present TraceMe, a predictive system utilizing advanced machine learning algorithms. The system employs YOLOv8 for efficient object detection, focusing on identifying vehicles in no-parking zones, and Tesseract OCR for accurate license plate recognition. The extracted license plate information is then processed by a machine learning model trained to predict the owner of the vehicle. The proposed system involves collecting and annotating a diverse dataset, training YOLOv8 and LPRNet model for vehicle number plate detection, utilizing Tesseract OCR for license plate extraction, and implementing a machine learning model for owner identification. Real-time processing and integration with surveillance systems allow for immediate identification of unauthorized parking incidents. The system generates alerts or notifications, aiding law enforcement in enforcing parking regulations.TraceMe not only provides a technological solution to mitigate unauthorized parking but also contributes to improved traffic management and public safety.

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition

Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.

A novel method for indian vehicle registration number plate detection and recognition using CNN

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as “extraction,” “pre-processing,” and “segmentation,” and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard.

NUMBER PLATE DETECTION USING MACHINE LEARNING

This project seeks to develop a real-time, Python-based, license plate recognition system leveraging deep learning's efficiency and robustness. Employing custom- trained convolutional neural networks alongside targeted object detection algorithms, the system aims to accurately locate and extract license plate information from images and video streams. Extensive data preprocessing and comprehensive model training optimize performance under varying lighting, angles, and minor occlusions. Integration into a user-friendly application further enhances accessibility and practical utility. This project's contribution lies in its applicability to real-world scenarios like traffic management, law enforcement, and vehicle tracking, demonstrating the power of advanced computer vision and machine learning for practical problem-solving in the automotive domain.

Automated License Plate Recognition for Non-Helmeted Motor Riders Using YOLO and OCR

One of the leading causes of serious injuries in motorcycle accidents is the failure to wear a helmet, pressing the need for effective measures to encourage riders to use helmets. To stop these frequent violations of business regulations, regular observation is necessary. The proposed system is for detecting traffic violations in India related to riding motorcycles without helmets. The system utilizes deep learning-based object detection using YOLO and OCR techniques to automatically detect non-helmet riders and extract license plate numbers. The proposed system aims to improve efficiency and accuracy in detecting violations by automating the process and reducing the need for manpower. The system involves three levels of object detection: person and motorcycle/moped, helmet, and license plate. OCR is used to extract the license plate number, and all techniques are subject to predefined conditions and constraints. The system is designed to operate on video input to ensure high-speed execution, and it intends to offer a complete solution for both detection of helmet and extracting the license plate number.

Implementation of number plate detection system for vehicle registration using IOT and recognition using CNN

In the intelligent transportation system the automatic license plate recognition and detection plays a very important role. This application could be used for traffic control security e-payment systems in the toll pay and parking. Many algorithms have been developed to force license plate detection and recognition and all have many advantages and flaws under different situations. With the advent and rise of deep learning concepts in various fields of artificial intelligence, computer vision has developed in leaps and bounds in terms of innovations and methods. Automatic License Plate Recognition has emerged as an effective method to automate the watch keeping process for traffic systems, parking fee structures, and police surveillance. License plate recognition (LPR) is a quite used and mature technology but much work is needed to be done in order to make it perfect. In recent years, the scientific community has made major advances in methodology and performance. This paper tries to aim at summarizing and analyzing various methodologies and progress in LPR in the deep learning era using IOT sensors. Hence, in this paper, an Automatic License Plate Detection and Recognition (ALPDR) system has been proposed having four steps namely License Plate Extraction, Image Pre-processing, Character Segmentation and Character Recognition. For the first three steps (extraction, pre-processing, and segmentation), unique methods have been proposed. As the character recognition is an important step of license plate recognition and detection, four different methods for character recognition have been experimented on, which include Convolution Neural Network (CNN), MobileNet, Inception V3, ResNet 50.

Text extraction and recognition method for license plates

Text extraction from images has always been challenging, especially if the image is taken under bad conditions, like lightning and noise that can influence text detection and recognition. This paper introduces a novel text extraction and recognition technique applied to the case study license plates. The main idea of this study is to detect the license plate in an input image and try to figure out the original country of the car based on the license plate. To accomplish this task, we first started collecting images from the internet, which were about 100 images. Afterward, we extracted the license plate using machine learning methods. Subsequently, we applied k-means clustering as well as thresholding in order to segment the extracted license plate and make the character recognition task easier. Thereafter, a sequence of techniques were applied, such as resizing and cropping the image to limit the wanted area of the desired character we want to extract. The last part of the proposed method is reading the text from the image using EasyOcr method, and using the function find in order to search for the character or the word. his proposed method achieved satisfactory results in detection where we achieved an accuracy of 87%, and a recognition of 97%. As for finding the ‘word’ part, the algorithm succeeded in all the examples.

Plate Number Recognition based on Hybrid Techniques

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

Efficient Thinning Algorithm for Malaysian Car Plate Character Recognition

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

Retracted: Image Recognition Technology Based on Machine Learning

With the development of machine learning for decades, there are still many problems unsolved, such as image recognition and location detection, image classification, image generation, speech recognition, natural language processing and so on. In the field of deep learning research, the research on image classification has always been the most basic, traditional and urgent research direction. At the same time, computer intelligent image recognition technology is also conducive to gradually better respond to the development of international indicators, and promote the development and progress of various fields. Therefore, image processing technology based on machine learning has been widely used in feature image, classification, segmentation and recognition, and is a hot spot in various fields. However, due to the complexity of video images and the distribution of objects in different application backgrounds, the classification accuracy becomes important and difficult. In the paper transportation industry, image recognition technology is applied to license plate recognition to extract license plate from complex background, segment license plate characters and recognize characters, and construct a machine learning non license plate automatic generation algorithm, which may improve the efficiency of non license plate recognition. The diversity and high generation speed of license plate training sample set can achieve the purpose of effectively training strong classifier. By using genetic algorithm to optimize BP neural network to classify license plate information, the anti-interference ability and license plate recognition accuracy are improved to a certain extent.

Modified ANPR using Neural Networks

Number Plate Recognition is a mass observation technique which is used to identify the vehicles. The identification and acknowledgement of a vehicle license plate is a key method in the greater part of applications related to vehicle movement. Moreover, it is a very famous and dynamic research subject in the field of image processing. Since every vehicle have a unique plate number, so if we have to perceive a specific vehicle we can utilize the license plate. The main objective of automatic vehicle number plate recognition is to design an efficient automatic authorized vehicle identification system by using the number plate. It has three modules namely license plate extraction, segmentation and recognition. . Different methods, techniques and algorithms have been developed to detect and recognize license plates. Nevertheless, due to the license plate characteristics that vary from one country to another in terms of numbering system, colours, language of characters, fonts and size. Further investigations are still needed in this field in order to make the detection and recognition process very efficient. Although this domain has been covered by a lot of researchers, various existing systems operate under well-defined and controlled conditions. For example, some frameworks require complicated hardware to make good quality images or capture images from vehicles with very slow speed. For this reason the detection and recognition of number plates in different conditions and under several climatic variations remains always difficult to realize with good results. For that, we present an automatic system for number plate detection and recognition based on convolutional neural networks. CNN has proved its robustness even with distorted, tilted and illuminated datasets.

SOFTWARE FOR RECOGNITION OF CAR NUMBER PLATE

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

Extraction of Character Regions through Machine Learning and Filtering

Characters in images are able to provide main information of the image. Therefore, it is important to analyze various kinds of image data and accurately extract the characters in images. This study proposes a new method of excluding background regions and accurately detecting character regions from input images with the uses of MCT features and Adaboost algorithm. The proposed method first extracts candidate character regions from input images with the uses of MCT features and Adaboost algorithm. It then excludes non-character regions and detects real character regions from the extracted candidate regions with the use of geometrical features. In the experiment of this study, the proposed method more robustly detected character regions from various input color images than a conventional method. For performance comparison, this study compared the method based on existing texture analysis and the proposed method. In this study, to qualitatively evaluate the performance of the proposed method of extracting license plate regions, the accuracy measure was defined. The measure is used to show the ratio of the accurately extracted character regions to all character regions of an image. The conventional method using the frequency factor-based texture information had many errors of character region detection, since it failed to execute binarization of background and character regions properly. On contrary, the proposed method made use of MCT features and Adaboost algorithm, effectively filtered candidate regions with the use of geometrical features, so that it detected character regions more accurately. The proposed character detection method is expected to be usefully applied to the fields of pattern recognition and image processing, such as store sign recognition and license plate recognition.

Text Character Extraction Implementation from Captured Handwritten Image to Text Conversionusing Template Matching Technique

Images contain various types of useful information that should be extracted whenever required. A various algorithms and methods are proposed to extract text from the given image, and by using that user will be able to access the text from any image. Variations in text may occur because of differences in size, style,orientation, alignment of text, and low image contrast, composite backgrounds make the problem during extraction of text. If we develop an application that extracts and recognizes those texts accurately in real time, then it can be applied to many important applications like document analysis, vehicle license plate extraction, text- based image indexing, etc and many applications have become realities in recent years. To overcome the above problems we develop such application that will convert the image into text by using algorithms, such as bounding box, HSV model, blob analysis,template matching, template generation.

Fuzzified Gabor filter for license plate detection

This paper presents a new algorithm for detecting and extracting license plates from a complex image by using a fuzzy two-dimensional Gabor filter. The filter parameters are fuzzified to optimize the Gabor filter. In particular, the orientation and wavelengths of the Gabor filter are fuzzified. Fuzzification of the wavelength results in greater selectivity. These parameters dominate the filtering results. Bell's function and triangular membership function were proven to be the most efficient method for selecting the filter parameters in our experiments and were used for fuzzification. The use of these filters provided satisfactory results; the components of interest were efficiently extracted, and the procedure was found to be very noise-resistant. A new algorithm was developed to detect license plates from low-resolution images.The algorithm uses fuzzy logic on a two-dimensional Gabor filter.The use of fuzzy logic allows the algorithm to perform under non-ideal conditions.Experiments were performed to validate the algorithm under various conditions.

Comparison of Feed Forward Network and Radial Basis Function for Detecting and Recognition of License Plate

One of the most important topics of intelligent transportation system is the License Plate Recognition (LPR). LPR systems have many potential applications in intelligent traffic systems, such as the payment of parking fee, highway toll fee, traffic data collection, traffic monitoring systems, traffic law enforcement, security control of restricted areas and so on. LPR was developed to identify vehicles by the contents of their license plates. The LPR system consists of three major modules: license plate extraction, segmentation and recognition of individual characters. This paper presents a study of applying the neural network approach for character image recognition. The new approach is tested on 400 samples of extracted license plate images captured in outdoor environment. The result yield 99.2% recognition accuracy, the method takes 1.6 seconds to perform the car plate recognition from vehicle's image. In order to decrease problems such as low quality and low contrast in the vehicle images, image recognition is done by the two different methods first is feed forward method and another is Radial Basis function and the best one is selected. The algorithm based on neural network which can quickly and correctly detect the region of license plate and the license plate detecting rate of success is 99.2%.

A License Plate Reader using AVR Microcontroller

Plate Recognizer is an efficient and effective system to identify the vehicles. In this paper we represent the simple and effective technique to identify the Indian license plate. This Proposed system consist tree steps, first is extraction of license plate from a captured image by camera. Second step is segmentation of the character from number plate and extract. And the last step is recognizing the characters and display it informs of text. So in this paper for first step used technique is morphological operation, for second step we used histogram to extract the numbers. For recognition we used the template matching by cross correlation between extracted character and database. At last to display recognized number plate at long distance on the LCD display AVR microcontroller is used.

Tunisian License Plate Number Recognition

Abstract License Plate Recognition (LPR) system plays a very important role in intelligent traffic control and security management, such as parking enforcement, stolen vehicle recovery and surveillance operations. This research describes a new method for the number recognition of Tunisian license plate. Based on low pass filter in license plate extraction module and network in LPR, the proposed method shows a great robustness in most unfavorable conditions.

안드로이드 기반 스마트 캠 방식의 저가형 자동차 번호판 인식 시스템 구현에 관한 연구

본 논문에서는 안드로이드 운영체제를 기반으로 하는 스마트 캠 방식의 저가형 자동차 번호판 인식 시스템을 제안한다. 제안하는 시스템은 휴대용 단말기와 서버로 구성된다. 단말기 하드웨어부는 ARM Cortex-A9(S5PV210) 프로세서로 이루어진 제어부, 전원부, 유무선통신부, 입출력부 등으로 구성된다. 단말기에 사용되는 카메라와 WiFi 모듈을 위한 리눅스 커널을 포팅하고 전용 디바이스 드라이버를 개발하였다. 번호판 인식 알고리즘은 캐니 에지검출기를 사용한 번호판 후보영역 설정, 레이블링을 이용한 번호판 번호 추출, 템플릿 매칭을 이용한 번호인식 등으로 구현된다. 단말기에 의하여 인식된 번호는 사용자가 소지한 휴대폰을 통하여 원격지의 서버에 전송되어 차량상태를 데이터베이스에서 검색하여 다시 단말기로 전송 해주게 된다. 본 논문에서 제안하는 시스템을 효용성을 입증하기 위하여 자연환경에서 사용자가 직접 단말기를 휴대하고 임의의 차량 번호판을 촬영하여 인식률을 확인한 결과, 95%의 인식률을 보였다. 제안된 시스템은 저가형의 휴대용 번호판 인식기에 적합하며, 안드로이드 운영체제를 사용함으로써 장기간 사용 시에도 시스템의 안정성을 가능케 하였다. In this paper, we propose a low-cost license plate recognition system based on smart cam system using Android. The proposed system consists of a portable device and server. Potable device Hardware consists of ARM Cortex-A9 (S5PV210) processor control unit, a power supply device, wired and wireless communication, input/output unit. We develope Linux kernel and dedicated device driver for WiFi module and camera. The license plate recognition algorithm is consisted of setting candidate plates areas with canny edge detector, extracting license plate number with Labeling, recognizing with template matching, etc. The number that is recognized by the device is transmitted to the remote server via the user mobile phone, and the server re-transfer the vehicle information in the database to the portable device. To verify the utility of the proposed system, user photographs the license plate of any vehicle in the natural environment. Confirming the recognition result, the recognition rate was 95%. The proposed system was suitable for low cost portable license plate recognition device, it enabled the stability of the system when used long time by using the Android operating system.

Area Extraction of License Plates Using an Artificial Neural Network

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plate's center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate covered by the learning pattern, the effect of suppression learning of the number and pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an under-ground parking garage demonstrated detection rates of 98.5%, 98.7%, and 100%, respectively.