computer-vision-7

License Plate Recognition

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in license-plate-recognition-7

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Dataset

Best Model

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Chinese License Plates

AOLP-RP

UFPR-ALPR

Libraries

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Use these libraries to find license-plate-recognition-7 models and implementations

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Most implemented papers

LPRNet: License Plate Recognition via Deep Neural Networks

Alexey Gruzdev, Sergey Zherzdev•Tue Jun 26 2018

The LPRNet algorithm may be used to create embedded solutions for LPR that feature high level accuracy even on challenging Chinese license plates, and is the first real-time License Plate Recognition system that does not use RNNs.

135

Content

UFPR-ALPR

SSIG-SegPlate

Caltech Cars

EnglishLP

UCSD-Stills

ChineseLP

AOLP

OpenALPR-EU

ChineseLP

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Vehicle and license plate recognition with novel dataset for toll collection

Saeed Anwar, Muhammad Usama, Hafeez Anwar, Muhammad Shahid, A. Anwar, Helmuth Hlavacs•Thu Feb 10 2022

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.

21 0

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Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution

Valfride Nascimento, Rayson Laroca, D. Menotti, W. R. Schwartz, Jorge de A. Lambert•Sun Oct 23 2022

A Single-Image Super-Resolution (SISR) approach that extends the attention/transformer module concept by exploiting the PixelShuftle layers capabilities and that has an improved loss function based on LPR predictions is presented.

16 0

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A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Rayson Laroca, D. Menotti, W. R. Schwartz, G. Gonçalves, L. A. Zanlorensi, E. Severo, Luiz Oliveira•Sun Feb 25 2018

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.

473 0

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Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers

Valfride Nascimento, Rayson Laroca, D. Menotti, W. R. Schwartz, Jorge de A. Lambert•Sun Apr 30 2023

A Single-Image Super-Resolution (SISR) approach that integrates attention and transformer modules to enhance the detection of structural and textural features in LR images and outperforms existing ones in both quantitative and qualitative measures is introduced.

22 0

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License Plate Detection and Recognition in Unconstrained Scenarios

C. Jung, S. Silva•Fri Sep 07 2018

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.

284 0

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Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks

Mohamed Yousef, K. Hussain, U. S. Mohammed•Sun Dec 30 2018

The proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function, which operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner.

133 0

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An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector

Rayson Laroca, D. Menotti, W. R. Schwartz, G. Gonçalves, L. A. Zanlorensi, E. Todt•Tue Sep 03 2019

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.

193 0

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Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution

M. Jeon, Younkwan Lee, Jiwon Jun, Yoojin Hong•Wed Oct 09 2019

Experimental results on AOLP and GIST-LP dataset illustrate that the proposed novel license plate recognition method, without any scene-specific adaptation, outperforms current LP recognition approaches in accuracy and provides visual enhancement in the SR results that are easier to understand than original data.

16 0

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Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

Rayson Laroca, D. Menotti, I. O. D. Oliveira, K. Fonseca, R. Minetto•Tue Nov 12 2019

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.

27 0

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