License plate recognition system

License Plate recognition (LPR) system is a key to many traffic related applications such as road traffic monitoring or parking lots access control. This paper proposes an automatic license plate recognition system for Saudi Arabian license plates. The system presents an algorithm for the extraction of license plate and segmentation of characters. Recognition is done using template matching. However the proposed work seems to be the first attempt towards the recognition of Saudi Arabian license plates. The performance of the system has been investigated on real images of about 710 vehicles captured under various illumination conditions. Recognition of about 96% shows that the system is quite efficient

A New Approach for Recognizing Saudi Arabian License Plates using Neural Networks

In this paper, a neural networks (NN) based automatic license plate recognition system (ALPR) is proposed for Saudi Arabian license plates with Arabic characters. The license plate region is rst localized by rst tracing the exterior and the interior close boundaries of objects in the car image and then separating the license plate by determining the rectangularity characteristic of these close objects. Character segmentation is performed via vertical and horizontal projection proles. Finally, a Multilayer Feedforward Neural Network (MFNN) with a backpropagation (BP) algorithm is used for character recognition. We discuss new features from the characters for training the NN. The results obtained from a medium size data base are very promising, i.e., 98%. The alogoritms discussed here were tested at the entrance of a praking lot to mimic a real life situation

A New Approach for Recognizing Saudi Arabian License Plates using Neural Networks

In this paper, a neural networks (NN) based automatic license plate recognition system (ALPR) is proposed for Saudi Arabian license plates with Arabic characters. The license plate region is rst localized by rst tracing the exterior and the interior close boundaries of objects in the car image and then separating the license plate by determining the rectangularity characteristic of these close objects. Character segmentation is performed via vertical and horizontal projection proles. Finally, a Multilayer Feedforward Neural Network (MFNN) with a backpropagation (BP) algorithm is used for character recognition. We discuss new features from the characters for training the NN. The results obtained from a medium size data base are very promising, i.e., 98%. The alogoritms discussed here were tested at the entrance of a praking lot to mimic a real life situation

An Intelligent Paper Currency Recognition System

AbstractPaper currency recognition (PCR) is an important area of pattern recognition. A system for the recognition of paper currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. This paper proposes an automatic paper currency recognition system for paper currency. A method of recognizing paper currencies has been introduced. This is based on interesting features and correlation between images. It uses Radial Basis Function Network for classification. The method uses the case of Saudi Arabian paper currency as a model. The method is quite reasonable in terms of accuracy. The system deals with 110 images, 10 of which are tilted with an angle less than 15o. The rest of the currency images consist of mixed including noisy and normal images 50 each. It uses fourth series (1984–2007) of currency issued by Saudi Arabian Monetary Agency (SAMA) as a model currency under consideration. The system produces accuracy of recognition as 95.37%, 91.65%, and 87.5%, for the Normal Non-Tilted Images, Noisy Non-Tilted Images, and Tilted Images respectively. The overall Average Recognition Rate for the data of 110 images is computed as 91.51%. The proposed algorithm is fully automatic and requires no human intervention. The proposed technique produces quite satisfactory results in terms of recognition and efficiency

License Plate Super-Resolution Using Diffusion Models

In surveillance, accurately recognizing license plates is hindered by their often low quality and small dimensions, compromising recognition precision. Despite advancements in AI-based image super-resolution, methods like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) still fall short in enhancing license plate images. This study leverages the cutting-edge diffusion model, which has consistently outperformed other deep learning techniques in image restoration. By training this model using a curated dataset of Saudi license plates, both in low and high resolutions, we discovered the diffusion model's superior efficacy. The method achieves a 12.55\% and 37.32% improvement in Peak Signal-to-Noise Ratio (PSNR) over SwinIR and ESRGAN, respectively. Moreover, our method surpasses these techniques in terms of Structural Similarity Index (SSIM), registering a 4.89% and 17.66% improvement over SwinIR and ESRGAN, respectively. Furthermore, 92% of human evaluators preferred our images over those from other algorithms. In essence, this research presents a pioneering solution for license plate super-resolution, with tangible potential for surveillance systems

An advanced combination of semi-supervised Normalizing Flow & Yolo (YoloNF) to detect and recognize vehicle license plates

Fully Automatic License Plate Recognition (ALPR) has been a frequent research topic due to several practical applications. However, many of the current solutions are still not robust enough in real situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector and Normalizing flows. The model uses two new strategies. Firstly, a two-stage network using YOLO and a normalization flow-based model for normalization to detect Licenses Plates (LP) and recognize the LP with numbers and Arabic characters. Secondly, Multi-scale image transformations are implemented to provide a solution to the problem of the YOLO cropped LP detection including significant background noise. Furthermore, extensive experiments are led on a new dataset with realistic scenarios, we introduce a larger public annotated dataset collected from Moroccan plates. We demonstrate that our proposed model can learn on a small number of samples free of single or multiple characters. The dataset will also be made publicly available to encourage further studies and research on plate detection and recognition.Comment: arXiv admin note: text overlap with arXiv:1802.09567 by other authors; text overlap with arXiv:2012.06737 by other authors without attributio

Regional stratigraphy, facies distribution, and hydrocarbons potential of the Oligocene strata across the Arabian Plate and Western Iran

Major global events during the Oligocene epoch included a climatic change from warm “greenhouse” to a cooler “icehouse” that was accompanied by the onset of Antarctic glaciation. These events led to decline in water temperature, salinity, nutrient supply and oxygen levels, and the extinction of some major fauna and flora. Within the study area, during this epoch, the shrinkage of the Neotethys and the development of the Paratethys, the collision of Arabia with Eurasia and the development of the Zagros mountains and opening of the Red Sea which led eventually to the separation of Arabia from Africa were witnessed. Oligocene sediments are absent from most parts of the Arabian Plate but are well-preserved in western and southwestern Iran. The most well-developed strata are the coral reefs of the Kirkuk Group in northern Iraq and the shallow water carbonates of the Asmari Formation in southwestern Iran. The study area also represents the birthplace of commercial hydrocarbons production in the Middle East from these sediments in Masjid-i-Sulaiman Field in Iran and Kirkuk Field in Iraq at the first decade of the last century. Future exploration for hydrocarbons potential should focus on identifying subsurface coral buildups or clastic strata that are equivalent to the Asmari Formation in Iran.Scopu

Research of Indonesian license plates recognition on moving vehicles

The recognition of the characters in the license plate has been widely studied, but research to recognize the character of the license plate on a moving car is still rarely studied. License plate recognition on a moving car has several difficulties, for example capturing still images on moving images with non-blurred results. In addition, there are also several problems such as environmental disturbances (low lighting levels and heavy rain). In this study, a novel framework for recognizing license plate numbers is proposed that can overcome these problems. The proposed method in this study: detects moving vehicles, judges the existence of moving vehicles, captures moving vehicle images, deblurring images, locates license plates, extracts vertical edges, removes unnecessary edge lines, segments license plate locations, Indonesian license plate cutting character segmenting, character recognition. Experiments were carried out under several conditions: suitable conditions, poor lighting conditions (dawn, evening, and night), and unfavourable weather conditions (heavy rain, moderate rain, and light rain). In the experiment to test the success of the license plate number recognition, it was seen that the proposed method succeeded in recognizing 98.1 % of the total images tested. In unfavorable conditions such as poor lighting or when there are many disturbances such as rain, there is a decrease in the success rate of license plate recognition. Still, the proposed method's experimental results were higher than the method without deblurring by 1.7 %. There is still unsuccessful in recognizing license plates from the whole experiment due to a lot of noise. The noise can occur due to unfavourable environmental conditions such as heavy rain