3260 papers • 126 benchmarks • 313 datasets
License Plate Detection is an image-processing technology used to identify vehicles by their license plates. This technology is used in various security and traffic applications.
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An automatic toll tax collection framework designed for challenging conditions, consisting of three sequential steps: vehicle type recognition, license plate localization, and license plate reading, using state-of-the-art YOLO models.
A robust and efficient ALPR system based on the state-of-the-art YOLO object detector is presented, employing simple data augmentation tricks such as inverted License Plates and flipped characters for character segmentation and recognition.
This paper presents a novel network model which can predict the bounding box and recognize the corresponding LP number simultaneously with high speed and accuracy, and demonstrates the model outperforms current object detection and recognition approaches in both accuracy and speed.
Inspired by the success of deep neural networks in various vision applications, DNNs are leveraged to learn high-level features in a cascade framework, which lead to improved performance on both detection and recognition.
The main contribution is the introduction of a novel Convolutional Neural Network capable of detecting and rectifying multiple distorted license plates in a single image, which are fed to an Optical Character Recognition (OCR) method to obtain the final result.
An efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate detection and layout classification to improve the recognition results using post-processing rules is presented.
This work introduces a novel dataset, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates.
An efficient and accurate framework to solve the license plate detection and recognition tasks simultaneously is presented, that is a lightweight and unified deep neural network, that can be optimized end-to-end and work in real-time.
A novel and applicable method for degraded license plate detection via vehicle-plate relation mining, which localizes the license plate in a coarse-to-fine scheme and predicts the quadrilateral bounding box in the local region by regressing the four corners of thelicense plate to robustly detect oblique license plates.
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