License Plate Recognition
16 papers with code • 10 benchmarks • 13 datasets
Benchmarks
These leaderboards are used to track progress in License Plate Recognition
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Datasets
CCPD
AOLP
UFPR-ALPR
SSIG-SegPlate
RodoSol-ALPR
ChineseLP
EnglishLP
Caltech Cars
Most implemented papers
LPRNet: License Plate Recognition via Deep Neural Networks
lyl8213/Plate_Recognition-LPRnet
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This paper proposes LPRNet - end-to-end method for Automatic License Plate Recognition without preliminary character segmentation.
Vehicle and License Plate Recognition with Novel Dataset for Toll Collection
The best Mean Average Precision (mAP@0. 5) of 98. 8% for vehicle type recognition, 98. 5% for license plate detection, and 98. 3% for license plate reading is achieved by YOLOv4, while its lighter version, i. e., Tiny YOLOv4 obtained a mAP of 97. 1%, 97. 4%, and 93. 7% on vehicle type recognition, license plate detection, and license plate reading, respectively.
Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution
The License Plate Recognition (LPR) field has made impressive advances in the last decade due to novel deep learning approaches combined with the increased availability of training data.
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers
As a result, the generated images have a Structural Similarity Index Measure (SSIM) of less than 0. 10.
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
ikigai-aa/Automatic-License-Plate-Recognition
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First, in the SSIG dataset, composed of 2, 000 frames from 101 vehicle videos, our system achieved a recognition rate of 93. 53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89. 80% and 93. 03%, respectively) and considerably outperforming previous results (81. 80%).
License Plate Detection and Recognition in Unconstrained Scenarios
sergiomsilva/alpr-unconstrained
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ECCV 2018
Despite the large number of both commercial and academic methods for Automatic License Plate Recognition (ALPR), most existing approaches are focused on a specific license plate (LP) region (e. g. European, US, Brazilian, Taiwanese, etc.
Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks
IntuitionMachines/OrigamiNet
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Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges.
An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector
This paper presents 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 (LP) detection and layout classification to improve the recognition results using post-processing rules.
Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution
brightyoun/LPSR-Recognition
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Although most current license plate (LP) recognition applications have been significantly advanced, they are still limited to ideal environments where training data are carefully annotated with constrained scenes.
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate.
