نتایج جستجو برای: license plate recognitions systems
تعداد نتایج: 1299757 فیلتر نتایج به سال:
In this paper, we consider the difficulties in identifying the license plate number of a vehicle, due to the over brightness of the headlights in the vehicle, the muddy condition of the plate. Optical Character Recognition (OCR) technique is used to solve these problems and gives the solution. We would like to state that vehicle license plate information got from an image or a sequence of image...
Since the early 2000s, life in cities has changed significantly due to Internet of Things (IoT). This concept enables developers integrate different devices collecting, storing, and processing a large amount data, enabling new services improve various professional personal activities. However, privacy issues arise with data generated, solutions based on blockchain technology smart contract have...
Automatic license plate recognition system is an image processing technology used to identify vehicles by their license plates. Such systems require the recognition of characters from the plate image. Artificial neural networks are commonly used to perform character recognition due to their high noise tolerance. Feed-Forward Neural Network (FFNN) can be used to recognize the characters from ima...
Using a stochastic approach, this paper explores and models the basic stochastic characteristics of freeway traffic behavior under a wide range of traffic conditions during peak Periods and then applies the models to short-term traffic speed prediction.Automated motion detection has become an increasingly important subject in traffic surveillance systems. Automatic video analysis from traffic s...
License plate recognition (LPR) has many applications in traffic monitoring systems. In this paper, a vertical edge matching based algorithm to recognize Korean license plate from input gray-scale image is proposed. The algorithm is able to recognize license plates in normal shape, as well as plates that are out of shape due to the angle of view. The proposed algorithm is fast enough, the recog...
Globally and locally, the number of vehicles is on rise. It becoming more challenging for authorities to track down specific vehicles. Automatic License Plate Recognition becomes an addition transportation systems automation. Where extraction vehicle license plate done without human intervention. Identifying precise place a through its from moving images image among crucial activities discovery...
License Plate Recognition (LPR) system for vehicles is an innovative and a very challenging area for research due to the innumerous plate formats and the nonuniform outdoor illumination conditions during which images are acquired. Thus, most approaches developed, work under certain restrictions such as fixed illumination, stationary background and limited speed. Algorithms developed for LPR sys...
Huge number of vehicles in modern cities makes the Traffic Surveillance a complex one. Therefore, it’s essential to establish an efficient Automatic License Plate Recognition (ALPR) system for Traffic scheduling and Management. In this paper we propose an Automatic License Plate Recognition System for Recognizing the License plate Number. To deals with the illumination problem, good pre-process...
Problem statement: License plate detection and recognition is an image-processing technique used to identify a vehicle by its license plate. This notable technology has got multiple applications in various traffic and security cases. To name but a few, toll roads, border control, security and car tracking are same of its applications. The main stage is the isolation of the license plate, from t...
Automatic license plate recognition (ALPR) used for many applications especially in security applications, including border control. However, more accurate and language-independent techniques are still needed. This work provides a new approach to identifying Arabic plates different formats, colors, even English characters. Numbers, characters, layouts with either 1-line or 2-line presented. For...
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