A two-stage character segmentation method for Chinese license plate

Tian, Jiangmin; Wang, Ran; Wang, Guoyou; Liu, Jianguo; Xia, Youchen

doi:10.1016/j.compeleceng.2015.02.014

Cited by 39 publications

(16 citation statements)

References 17 publications

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“…Also in this dataset, 20% of the training images were employed for validation. Regarding the ChineseLP dataset, we did not find any previous work in which it was split into training/test sets, that is, all its images were used either to train or to test the methods proposed in [12,19,58,59], often jointly with other datasets. Thus, we adopted the same protocol of the SSIG-SegPlate and UFPR-ALPR datasets, in which 40% of the images are used for training, 40% for testing and 20% for validation.…”

Section: Evaluation Protocolmentioning

confidence: 99%

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)

415

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: Evaluation Protocolmentioning

confidence: 99%

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)

415

271

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“…Этот рост обусловлен присутствием в реальных изображениях артефактов, недоступных при генерации. 4 Li H., Shen C. Op. cit.…”

Section: рис 2 примеры генерируемых изображенийunclassified

“…Системы автоматического распознавания номеров (ALPR) используются для автоматического контроля скорости движения, идентификации угнанных транспортных средств, контроля доступа транспортных средств в частных помещениях и взимания платы за проезд 1 [1,2,3]. Однако большинство существующих алгоритмов 2, 3 работают только для конкретного шаблона номерного знака или со сложными системами захвата изображений, требовательны к условиям освещения и типам транспортных средств [4,5,6].…”

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Automatic license-plate recognition

Полтавский

Yurushkina

Yurushkin

2020

Vestnik Donskogo gosudarstvennogo tehničeskogo universiteta

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“…Now a plenty of work has been dedicated to the character segmentation rather than the recognition in this system. [3][4][5] In general, the segmentation strategies for characters, especially for touching characters, can be categorized into two major groups, that is, character-based and recognition-based segmentations. For the characterbased segmentation, only geometric attributes of characters are taken into account.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of touching characters via tree-indexed demarcation using forward and backward searches

Tang

Zhang

et al. 2017

Advances in Mechanical Engineering

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In this article, a tree-indexed demarcation using forward and backward searches is proposed to segment the seriously touching characters, which is used as an important procedure for the character recognition in the real industrial scenarios. Unlike the segmentation directly depending on the histogram analysis, the reliable and unreliable valleys are first identified from a smoothed projection curve in our method, where a set of image pre-procedures are carefully carried out to catch this smoothed curve. With these operations, two segment binary trees are sequently constructed via the corresponding forwarding and backward searches of the valleys. Then, the delimiter candidates are sophisticatedly selected with a number of well-designed rules. The optimal segmented paths are finally obtained on the width variation measure of the characters, and the isolated characters are achieved. Experiments show that with our tree-indexed demarcation using forward and backward searches method, the touching character groups can be effectively segmented to better serve the industrial automatic recognition system.

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A two-stage character segmentation method for Chinese license plate

Cited by 39 publications

References 17 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

Automatic license-plate recognition

Segmentation of touching characters via tree-indexed demarcation using forward and backward searches

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