Segmentation- and Annotation-Free License Plate Recognition With Deep Localization and Failure Identification

Bulan, Orhan; Kozitsky, Vladimir; Ramesh, Palghat; Shreve, Matthew

doi:10.1109/tits.2016.2639020

Cited by 180 publications

(91 citation statements)

References 34 publications

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“…The approaches developed for ALPR are still limited in various ways. Many authors only addressed part of the ALPR pipeline, e.g., LP detection [28,32,36] or character/LP recognition [33,37,38], or performed their experiments on private datasets [9,14,38], making it difficult to accurately evaluate the presented methods. Note that works focused on a single stage do not consider localization errors (i.e., correct but not so accurate detections) in earlier stages [10,33].…”

Section: Final Remarksmentioning

confidence: 99%

“…segmentation, it has become common the use of segmentation-free approaches for LP recognition [8][9][10][11], as the character segmentation by itself is a challenging task that is prone to be influenced by uneven lighting conditions, shadows, and noise [12].…”

Section: Introductionmentioning

confidence: 99%

“…Even though most authors claim that their approaches could be extended with small modifications to detect/segment/recognize LPs of different layouts [14,[19][20][21], this may not be an easy task. For instance, a character segmentation approach designed for LPs with simple backgrounds is likely to fail on LPs with complex backgrounds and logos that touch and overlap some characters (e.g., Florida LPs) [9].…”

Section: Introductionmentioning

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)

433

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: Final Remarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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)

433

271

View full text Add to dashboard Cite

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“…Li et al [20] extract deep feature representations by using RNN with LSTM for acquiring sequential features of the license plate. Bulan et al [2] estimate domain shifts between target and multiple source domains for selecting a domain that yields the best recognition performance based on fully convolutional network [23]. However, these methods only consider high-quality license plate image except for low-quality image, which is easily led to low performance in real-world scenes.…”

Section: License Plate Recognitionmentioning

confidence: 99%

“…In the last few years, LPR has been widely studied in theoretical, experimental and numerical ways to provide robust image representation. Many LPR methods [2,1,11,20] are capable of capturing the structural properties of images and noise for carefully constrained settings. Despite the recent success, recognizing license plate in the wild is still far from satisfactory due to the variations that suffer from appearance, noise, angle, and illumination.…”

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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Towards Human-Level License Plate Recognition

Zhuang

Hou

Wang

et al. 2018

Lecture Notes in Computer Science

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Segmentation- and Annotation-Free License Plate Recognition With Deep Localization and Failure Identification

Cited by 180 publications

References 34 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

Towards Human-Level License Plate Recognition

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