Highway travel time analysis using license plate image capture techniques

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.

In this paper, we evaluate the influence of detection quality on OCR accuracy in automatic license plate recognition (ALPR) systems. Using a modular pipeline, we compare the performance of three OCR engines - Tesseract, EasyOCR, and PaddleOCR -when fed with outputs from two variants of YOLOv8n object detectors: one autotuned using Optuna and another manually fine-tuned. The fine-tuned YOLOv8n model, optimized for Indian license plates, achieved the best results, allowing PaddleOCR to reach an OCR accuracy of 99 %, marking a 4.16 % improvement over the baseline autotuned model. We use a range of evaluation metrics, including Character Error Rate (CER), Word Error Rate (WER), and Normalized Levenshtein Distance (NLD), to comprehensively quantify OCR performance under different detection scenarios. Our findings highlight the critical role of accurate detection in downstream OCR quality and offer valuable insights for building robust, end-to-end ALPR systems, particularly in real-world deployment scenarios.

Vehicle license plate recognition has been a much studied research area in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is rather different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, feature extraction and neural networks. The image processing library is developed in-house which we referred to as vision system development platform (VSDP). The Kirsch edge feature extraction technique is used to extract features from the license plates characters which are then used as inputs to the neural network classifier. The neural network model is the standard multilayered perceptron trained using the back-propagation algorithm. The prototyped system has an accuracy of about 91%, however, suggestions to further improve the system are discussed in this paper based on the analysis of the error.

Traffic control and vehicle owner identification become major problems in Bangladesh. Most of the time it is difficult to identify the driver or the owner of the vehicles who violate the traffic rules or do any accidental work on the road. Moreover, it is very time-consuming for a traffic police officer to physically check the license plate of every vehicle. So, an automatic license plate recognition system is a much-needed solution to solve these problems. The existing Bangla license plate recognition systems are mostly based on character segmentation and these methods are not implemented in real-time. In this study, two separate Deep Convolutional Neural Network (DCNN) models are used to identify the license plate and the characters on the license plate from the real-time video streaming. The first CNN model detects the license plate from the live video of a vehicle on the road. Than it crop the license plate area from the video frames. The cropped frame is then fed into the second CNN to detect the characters on that license plate. The characters are detected as individual objects. After detecting all the characters and numbers on the license plate, they are rearranged according to their position on the plate. To train the proposed model total of 292 images are collected used. Moreover, an open-sourced Bangla handwritten character dataset named BanglaLekha-Isolated is also used to train the model with synthetic character data. The trained model is tested using 18 live videos and 6 still image data. Finally, the proposed methodology gains a 100% precision on detecting the license plate, and 91.67% precision for detecting the characters on the license plate for the given test dataset.

Automatic license plate recognition is a technology that relies on optical character recognition methods used on images for reading license plate text. These techniques are, in general, used with image processing techniques, whether the system is composed of them entirely or in addition to deep learning techniques and neural network models. Such a system can be used by CCTV cameras, smart cameras, and other such devices, for tasks in areas like law enforcement, toll collection systems, or parking lot ticket systems. These systems can be used to capture images, locate license plates and recognize the text from the license plate. Depending on the region, post-processing can be conducted to further refine the recognized text based on a set of predefined rules, such as the position of letters and numbers in the license plate string. This work aims to create an automatic license plate recognition system with the use of image processing techniques, mainly from the OpenCV python library. The system will attempt to recognize license plate text from vehicles parked in parking lots. This can be useful for people, who do not remember the place where they parked their vehicle, or in other areas such as law enforcement. For large parking lots, there are generally camera systems already in place, in this case, the system should be able to work with multiple angles and views of the parking lot to get results with the highest accuracy ratings.

The increasing number of motor vehicles in Indonesia demands technological solutions to enhance efficiency and security, particularly in automatic license plate recognition systems. This study aims to develop an automatic license plate detection system using the YOLOv11 algorithm to detect license plates and their characters in real-time. The research methodology includes collecting datasets from Kaggle, RoboFlow, and manual acquisition, followed by annotation, data augmentation, model training, and interface development using Tkinter and OpenCV. The dataset comprises 4000 license plate images and 3000 characters images, divided for training, validation, and testing. Evaluation results demonstrate strong model performance, with precision of 0.891, recall of 0.911, mAP50 of 0.906, and mAP50-95 of 0.631 for license plate detection, and precision of 0.889, recall of 0.912, mAP50 of 0.907, and mAP50-95 of 0.629 for character detection. Real-time testing showed that 12 out of 12 license plates were successfully recognized, influenced by lighting conditions, distance, and plate orientation. This study produced an efficient system for parking security, with potential for further development.

A new algorithm for license plate character recognition system is proposed on the basis of Signature analysis properties and features extraction. Signature analysis has been used to locate license plate region and its properties can be further utilised in supporting and affirming the license plate character recognition. This paper presents the implementation of Signature Analysis combined with Features Extraction to form feature vector for each character with a length of 56. Implementation of these two methods is used in tracking of vehicle’s automatic license plate recognition system (ALPR). The developed ALPR comprises of three phase. The recognition stage utilised the vector to be trained in a simple multi-layer feed-forward back-propagation Neural Network with 56 inputs and 34 neurons in its output layer. The network is trained with both ideal and noisy characters. The results obtained show that the proposed system is capable to recognise both ideal and non-ideal license plate characters. The system also capable to tackle the common character misclassification problems due to similarity in characters.

With the advances in sensor and data transfer technologies, the usage areas of Automatic License Plate Recognition (ALPR) systems have been expanded in the public and private sectors. In public safety, ALPR systems are used to monitor and control traffic data at both individual and collective levels. To build an efficient sensor network, the locations of ALPR systems should be determined optimally. This study provides an approach to determine optimal locations of ALPR systems that maximize network coverage consisting of two measures: i) vehicle coverage and ii) road coverage. The former represents the daily average vehicle flow whereas the latter stands for the number of road-links covered. The relative importance of vehicle and road coverages are taken into consideration, and optimal solutions under various scenarios are presented. A close neighbor constraint is introduced to avoid inefficient distribution of ALPR systems on the network. A case study with numerical examples designed for two cities in Turkey is provided. The centralized and decentralized solutions are compared against the current state, and the results show that the network coverage increases substantially in the centralized case.

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 growing research area as with the increase in the number of vehicles, requirements for high accuracy ALPR systems are increasing as it has been extensively used for automatic road tax collection, monitor unlike activities etc. Character segmentation is a major step in the ALPR system. This paper includes the use of image pre-processing techniques, K-means clustering, Otsu and Niblack method and Connected Component Analysis (CCA) to perform character segmentation. Unlike existing works which are based on license plates of other countries and therefore may not perform well on Indian license plates, in this work we have dealt with Indian license plates. Many of the existing works are based on license plates that are captured from a straight angle and good illumination and hence may not perform well if the images of license plates have noises or are captured at different angles. Therefore, in this work we have used license plates which are at different angles with different lighting conditions.

Automatic license plate detection (ALPD) and recognition systems are becoming ever more widely used in the world. At present, most of the ALPD systems for Chinese license plates(LPs) are either slow or less accurate, which can affect the subsequent stage of license plate character recognition. In this paper we propose a fast and accurate ALPD system for images taken in the wild. We first design and train a YOLO-like [14] but simpler network for fast LP localization; then we develop another network to find the four corner points of the LP within the previously selected rectangular area; finally we use perspective projection to convert the LP to the front view for easy subsequent recognition. CCPD [2] dataset is used for training the networks. Experimental results show two distinctive advantages of the proposed method. First, the proposed method is both very accurate and robust. The recognition accuracy rate has reached 98.6% over a wide range of test images, especially those image types not contained in the training set. Secondly, the proposed method is very fast. The whole LP detection and recognition system can process images of size 720P at the rate of 33.4 frames per second, far exceeding previously reported performance in terms of both accuracy and speed. In addition, we find that many of the LP corner labels are not accurate yet the output from our method give very accurate LP corner coordinates. So we use our network to correct the LP labels in the CCPD dataset.

The rapid development of urban intelligence has turned intelligent transport system (ITS) development into a primary goal of traffic management. Automated license plate recognition (ALPR) for moving vehicles is a core aspect of ITS. Most ALPR systems send images back to a server for license plate recognition. To reduce delays and bandwidth use during image transmission, this study proposes an edge-AI-based real-time ALPR (ER-ALPR) system, in which an AGX XAVIER embedded system is embedded on the edge of a camera to achieve real-time image input to an AGX edge device and to enable real-time automatic license plate character recognition. To assess license plate characters and styles in a realistic setting, the proposed ER-ALPR system applies the following approaches: (1) image preprocessing; (2) You Only Look Once v4-Tiny (YOLOv4-Tiny) for license plate frame detection; (3) virtual judgment line for determining whether a license plate frame has passed; (4) the proposed modified YOLOv4 (M-YOLOv4) for license plate character recognition; and (5) a logic auxiliary judgment system for improving license plate recognition accuracy. We tested the proposed ER-ALPR system in selected real-life test environments in Taiwan. In experiments, the proposed ER-ALPR system achieved license plate character recognition rates of 97% and 95% in the day and at night, respectively. Through the AGX system, the proposed ER-ALPR system achieves a high recognition rate at a low computational cost.

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.

Automatic License plate detection and recognition system is special form of optical character recognition and has been an active research domain in image processing field. However, the accuracy is varied due to different styles of number plates endorsed. Besides, the characters on Malaysia license plate are in one or two lines. Thus, the proposed license plate recognition (LPR) technique of this research is able to achieve the best recognition performance based on Malaysia license plate vehicle registration number characters. This paper presents a study of applying the template matching method for character image recognition. The database of characters and license plate image has been created by collecting images from various type of car. The initial pre-processing involves image enhancement, binarization, filtering and segmenting of license plate. There are 100 license plates that contain 693 characters have been tested, and the result shown that 92.78 % of all characters is correctly recognized. Thus, template matching can be classified as one of the promising algorithm for recognizing Malaysia license plate.