38,824 research outputs found

    Empirical Study of Car License Plates Recognition

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    The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison

    Research of Indonesian license plates recognition on moving vehicles

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    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

    PENCOCOKAN PLAT KENDARAAN DENGAN ALGORITMA HAARCASCADE DAN TEMPLATE MATCHING

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    The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card fora vehicle. It can map to the owner and further information about vehicle. License plate information is useful to helptraffic management systems. For example, traffic management systems can check for vehicles moving at speeds notpermitted by law and can also be installed in parking areas to secure the entrance or exit way for vehicles. Licenseplate recognition algorithms have been proposed by many researchers. License plate recognition requires licenseplate detection, segmentation, and characters recognition. The algorithm detects the position of a license plate andextracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm hasits strengths and weaknesses. In this thesis, I implement Haar-cascade algorithm for detecting license plates andTemplate matching algorithm for recognizing license plate characters. I evaluate each of these algorithms on thesame two datasets, one from Greece and one from Thailand. For detecting license plates, Haar cascade algorithmobtained 84% and 86.5%. After the best result of license plate detection is obtained, for the segmentation part aLaplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that templatematching obtained good accuracy on both datasets

    Vehicle Number Plate Recognition with Bilinear Interpolation and Plotting Horizontal and Vertical Edge Processing Histogram with Sound Signals

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    The Vehicle Number Plate Recognition is a system designed to help in recognition of number plates of vehicles. This type of system is designed for the objective of the security system. Vehicle Number Plate Recognition is based on the Image Processing system. Vehicle Number Plate Recognition helps in the functions like detection of the number plates of the car, processing them and using processed data for further processes like storing. The system is simulated and implemented in MATLAB, and its performance is tested on the real image. It is assumed that images of the vehicle have been captured from Digital Camera or Mobile Phones. Alphanumeric Characters on the plate has been extracted using the Template Images of Alphanumeric characters. Many times it becomes very difficult to identify the owner of the Vehicle who violates the traffic rules and drives the vehicle so fast. Therefore, it is difficult to catch and punish those people because the traffic personal might not be able to retrieve the vehicle number from the moving vehicle because of fast speed of the vehicle. Therefore, there is a need to develop Vehicle Number Plate Recognition (VNPR) system as this is one of the best solution to this problem

    Low-cost automatic number plate detection system

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    Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2022The ability to detect vehicle license plates has been implemented to reduce traffic violations with varying success. Some of these applications have limited coverage or reduced functionality when used in real-time, as some were only tested with still images and datasets. In this paper, I propose a technique for implementing the Automatic Number Plate Recognition (ANPR) System using Python and Open Computer Vision Library integrated with a traffic light to capture and interpret the number plate of moving vehicles in different lighting conditions and at varying times speeds. The system is 100% successful at detecting vehicle number plates for cars traveling at 5kmph.Ashesi Universit

    Determining the relative position of vehicles considering bidirectional traffic scenarios in VANETS

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    Researchers pertaining to both academia and industry have shown strong interest in developing and improving the existing critical ITS solutions. In some of the existing solutions, specially the ones that aim at providing context aware services, the knowledge of relative positioning of one node by other nodes becomes crucial. In this paper we explore, apart from the conventional use of GPS data, the applicability of image processing to aid in determining the relative positions of nodes in a vehicular network. Experiments conducted show that both the use of location information and image processing works well and can be deployed depending on the requirement of the application. Our experiments show that the results that used location information were affected by GPS errors, while the use of image processing, although producing more accurate results, require significantly more processing power
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