Towards Human-Level License Plate Recognition

Zhuang, Jiafan; Hou, Saihui; Wang, Zhong Lin; Zha, Zheng-Jun

doi:10.1007/978-3-030-01219-9_19

Cited by 53 publications

(98 citation statements)

References 23 publications

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“…Zhuang et al [33] proposed a semantic segmentation technique followed by a character count refinement module to recognize the characters of an LP. For semantic segmentation, they simplified the DeepLabV2 (ResNet-101) model by removing the multi-scaling process, increasing computational efficiency.…”

Section: License Plate Recognitionmentioning

confidence: 99%

“…In this work, the proposed end-to-end system is evaluated in eight public datasets that present a great variety in the way they were collected, with images of various types of vehicles (including motor-cycles) and numerous LP layouts. It should be noted that, in most of the works in the literature, no more than three datasets were used in the experiments (e.g., [12,17,18,33]). In addition, despite the fact that motorcycles are one of the most popular transportation means in metropolitan areas [39], motorcycle images have not been used in the assessment of most ALPR systems in the literature.…”

Section: Final Remarksmentioning

confidence: 99%

“…Most of the approaches are not capable of recognizing LPs in real time (i.e., 30 FPS) [7,15,33], making it impossible for them to be applied in some applications. Furthermore, several authors do not report the execution time of the proposed methods or report the time required only for a specific stage [12,14,38], making it difficult an accurate analysis of their speed/accuracy trade-off, as well as their applicability.…”

Section: Final Remarksmentioning

confidence: 99%

See 2 more Smart Citations

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

Laroca

Severo

Zanlorensi

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

413

271

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In this paper, we present an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the stateof-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. The system is conceived by evaluating and optimizing different models with various modifications, aiming at achieving the best speed/accuracy trade-off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end-to-end recognition rate of 96.8% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. Our system also achieved impressive frames per second (FPS) rates on a high-end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that we manually labeled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community.

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Section: License Plate Recognitionmentioning

confidence: 99%

Section: Final Remarksmentioning

confidence: 99%

Section: Final Remarksmentioning

confidence: 99%

See 1 more Smart Citation

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

Laroca

Severo

Zanlorensi

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

413

271

View full text Add to dashboard Cite

show abstract

“…We compare the proposed method with some state-ofthe-art LPR methods [13,21,35,41]. For the baseline LPR, the SNIDER has been evaluated over the two datasets as described in Section 4.1 that contain low-quality license plate images with a variety of geometric variations.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…As a result, CNN-guided LPR methods are also extensively applied to handle the problem of recognizing license plate captured directly real-world camera. For example, Zhuang et al [41] transform license plate into a semantic segmentation result with the counting network to handle appearance variations. Although numerous LPR methods have been developed [35,41], they are not still capable of learning all types of samples in the wild.…”

Section: Introductionmentioning

confidence: 99%

SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition

Lee

Ahn

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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In this paper, we present an algorithm for real-world license plate recognition (LPR) from a low-quality image. Our method is built upon a framework that includes denoising and rectification, and each task is conducted by Convolutional Neural Networks. Existing denoising and rectification have been treated separately as a single network in previous research. In contrast to the previous work, we here propose an end-to-end trainable network for image recovery, Single Noisy Image DEnoising and Rectification (SNIDER), which focuses on solving both the problems jointly. It overcomes those obstacles by designing a novel network to address the denoising and rectification jointly. Moreover, we propose a way to leverage optimization with the auxiliary tasks for multi-task fitting and novel training losses. Extensive experiments on two challenging LPR datasets demonstrate the effectiveness of our proposed method in recovering the high-quality license plate image from the low-quality one and show that the the proposed method outperforms other state-of-the-art methods.

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An Effective Method for Yemeni License Plate Recognition Based on Deep Neural Networks

Taleb

Yuan

et al. 2022

Intelligent Computing Methodologies

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Towards Human-Level License Plate Recognition

Cited by 53 publications

References 23 publications

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

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

SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition

An Effective Method for Yemeni License Plate Recognition Based on Deep Neural Networks

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