IR-LPR: Large Scale of Iranian License Plate Recognition Dataset
Malek Ashtar University of Technology（马利克·阿什塔尔工业大学）
Mahdi Rahmani, Melika Sabaghian, Seyyede Mahila Moghadami, Mohammad Mohsen Talaie, Mahdi Naghibi, Mohammad Ali Keyvanrad
备注：
This is the final draft for the paper submitted to the 12th International Conference on Computer and Knowledge Engineering (ICCKE 2022), Ferdowsi University of Mashhad, Iran
Object detection has always been practical. There are so many things in our world that recognizing them can not only increase our automatic knowledge of the surroundings, but can also be lucrative for those interested in starting a new business. One of these attractive objects is the license plate (LP). In addition to the security uses that license plate detection can have, it can also be used to create creative businesses. With the development of object detection methods based on deep learning models, an appropriate and comprehensive dataset becomes doubly important. But due to the frequent commercial use of license plate datasets, there are limited datasets not only in Iran but also in the world. The largest Iranian dataset for detection license plates has 1,466 images. Also, the largest Iranian dataset for recognizing the characters of a license plate has 5,000 images. We have prepared a complete dataset including 20,967 car images along with all the detection annotation of the whole license plate and its characters, which can be useful for various purposes. Also, the total number of license plate images for character recognition application is 27,745 images.
This paper proposes LPRNet - end-to-end method for Automatic License Plate Recognition without preliminary character segmentation. Our approach is inspired by recent breakthroughs in Deep Neural Networks, and works in real-time with recognition accuracy up to 95% for Chinese license plates: 3 ms/plate on nVIDIA GeForce GTX 1080 and 1.3 ms/plate on Intel Core i7-6700K CPU. LPRNet consists of the lightweight Convolutional Neural Network, so it can be trained in end-to-end way. To the best of our knowledge, LPRNet is the first real-time License Plate Recognition system that does not use RNNs. As …
License plate recognition systems have a very important role in many applications such as toll management, parking control, and traffic management. In this paper, a framework of deep convolutional neural networks is proposed for Iranian license plate recognition. The first CNN is the YOLOv3 network that detects the Iranian license plate in the input image while the second CNN is a Faster R-CNN that recognizes and classifies the characters in the detected license plate. A dataset of Iranian license plates consisting of ill-conditioned images also developed in this paper. The YOLOv3 network achi…
Universiti Teknikal Malaysia Melaka (UTeM)（马来西亚马六甲技术大学）
Most vehicle license plate recognition use neural network techniques to enhance its computing capability. The image of the vehicle license plate is captured and processed to produce a textual output for further processing. This paper reviews image processing and neural network techniques applied at different stages which are preprocessing, filtering, feature extraction, segmentation and recognition in such way to remove the noise of the image, to enhance the image quality and to expedite the computing process by converting the characters in the image into respective text. An exemplar experimen…
Federal University of Paraná（巴拉那联邦大学）；Federal Institute of Paraná（巴拉那联邦理工学院）；Federal University of Ouro Preto（欧鲁普雷图联邦大学）
Public datasets have played a key role in advancing the state of the art in License Plate Recognition (LPR). Although dataset bias has been recognized as a severe problem in the computer vision community, it has been largely overlooked in the LPR literature. LPR models are usually trained and evaluated separately on each dataset. In this scenario, they have often proven robust in the dataset they were trained in but showed limited performance in unseen ones. Therefore, this work investigates the dataset bias problem in the LPR context. We performed experiments on eight datasets, four collected…
We evaluated a lightweight Convolutional Neural Network (CNN) called LPRNet [1] for automatic License Plate Recognition (LPR). We evaluated the algorithm on two datasets, one composed of real license plate images and the other of synthetic license plate images. In addition, we compared its performance against Tesseract [2], an Optical Character Recognition engine. We measured performance based on recognition accuracy and Levenshtein Distance. LPRNet is an end-to-end framework and demonstrated robust performance on both datasets, delivering 90 and 89 percent recognition accuracy on test sets of…
Federal University of Paraná（巴拉那联邦大学）；Brazilian Federal Police（巴西联邦警察）；Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）
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. However, it still has some open issues, especially when the data come from low-resolution (LR) and low-quality images/videos, as in surveillance systems. This work focuses on license plate (LP) reconstruction in LR and low-quality images. We present a Single-Image Super-Resolution (SISR) approach that extends the attention/transformer module concept by exploiting the capabilities of PixelShuffle layers and tha…
Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition
Federal University of Paraná（巴拉那联邦大学）；Federal Institute of Paraná（巴拉那联邦学院）；Pontifical Catholic University of Paraná（巴拉那天主教大学）；Federal University of Technology-Paraná（巴拉那联邦技术大学）
SVS College of Engineering and Technology（SVS工程技术学院）；SSK College of Engineering & Technology（SSK工程技术学院）
It is believed that there are currently millions of vehicles on the roads worldwide. The over speed of vehicles,theft of vehicles, disobeying traffic rules in public, an unauthorized person entering the restricted area are keep on increasing. In order restrict against these criminal activities, we need an automatic public security system. Each vehicle has their own Vehicle Identification Number (VIN) as their primary identifier. The VIN is actually a License Number which states a legal license to participate in the public traffic. The proposed paper is to identify the vehicle with the help of …
Indian Licence Plate Detection is a problem that has not been explored much at an open-source level.There are proprietary solutions available for it, but there is no big open-source dataset that can be used to perform experiments and test different approaches.Most of the large datasets available are for countries like China, Brazil, but the model trained on these datasets does not perform well on Indian plates because the font styles and plate designs used vary significantly from country to country.This paper introduces an Indian license plate dataset with 16192 images and 21683 plate plates a…
Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach
Federal University of Paraná（巴拉那联邦大学）；Pontifical Catholic University of Paraná（巴拉那天主教大学）；Brazilian Federal Police（巴西联邦警察）；Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）
Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark
Federal University of Paraná（巴西南里奥格兰德联邦大学）；Brazilian Federal Police（巴西联邦警察）；Federal University of Minas Gerais（巴西矿务联邦大学）；Pontifical Catholic University of Paraná（帕尔米拉天主教大学）
Evaluating Vision-Language Models as a Zero-Shot Learning Alternative to You Only Look Once and Optical Character Recognition for Nigerian License Plate Recognition
Ahmadu Bello University（艾哈迈杜·贝罗大学）；Bayero University Kano（卡诺贝罗大学）
Gwangju Institute of Science and Technology (GIST)（光州科学技术院）
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…
Nowadays in developing or developed countries, the Intelligent Transportation System (ITS) technology has attracted so much attention to itself. License Plate Recognition (LPR) systems have many applications in ITSs, such as the payment of parking fee, controlling the traffic volume, traffic data collection, etc. This paper presents a new and fast method for license plate extraction based on edge analysis. our proposed method consist of four stage, which are edge detection, non-useable edge and noise removing, edge analysis and morphology-based license plate extraction. In the result part, the…
In this paper, we propose an automatic and mechanized license and number plate recognition (LNPR) system which can extract the license plate number of the vehicles passing through a given location using image processing algorithms. No additional devices such as GPS or radio frequency identification (RFID) need to be installed for implementing the proposed system. Using special cameras, the system takes pictures from each passing vehicle and forwards the image to the computer for being processed by the LPR software. Plate recognition software uses different algorithms such as localization, orie…
Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation
Pontifical Catholic University of Paraná (PUCPR), Curitiba, Brazil；Federal University of Paran\'a (UFPR), Curitiba, Brazil；Federal Institute of Paran\'a (IFPR), Irati, Brazil；Federal University of Ouro Preto (UFOP), Ouro Preto, Brazil；Federal University of Technology-Paran\' a (UTFPR), Curitiba, Brazil
Next-Generation License Plate Detection and Recognition System using YOLOv8
School of Electrical Engineering；Computer Science (SEECS), \ University of Sciences；Ghulam Ishaq Khan Institute of Engineering Sciences \ Technology (GIKI), Topi, District, Swabi, \ Pakhtunkhwa 23460, Pakistan, Email:prorector\
License Plate Recognition (LPR) plays an important role on the traffic monitoring and parking management. A robust and efficient method for enhancing accuracy of license plate characters recognition based on K Nearest Neighbours (K-NN) classifier is presented in this paper. The system first prepares a contour form of the extracted character, then the angle and distance feature information about the character is extracted and finally K-NN classifier is used to character recognition. Angle and distance features of a character have been computed based on distribution of points on the bitmap image…
Recent advances in deep neural networks (DNNs) have significantly enhanced the capabilities of optical character recognition (OCR) technology, enabling its adoption to a wide range of real-world applications. Despite this success, DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the adversary can influence the DNN model's prediction by carefully manipulating input to the model. Prior work has demonstrated the security impacts of adversarial attacks on various OCR languages. However, to date, no studies have been conducted and evaluated on an OCR system tailored specific…
The Intelligent Transportation Systems (ITS) are the subject of a world economic competition. They are the application of new information and communication technologies in the transport sector, to make the infrastructures more efficient, more reliable and more ecological. License Plates Recognition (LPR) is the key module of these systems, in which the License Plate Localization (LPL) is the most important stage, because it determines the speed and robustness of this module. Thus, during this step the algorithm must process the image and overcome several constraints as climatic and lighting co…
LPTR-AFLNet: Lightweight Integrated Chinese License Plate Rectification and Recognition Network
Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University（湖北水利水电工程智能视觉监测重点实验室，中国三峡大学）；College of Computer and Information Technology, China Three Gorges University（计算机与信息学院，中国三峡大学）；Hubei Key Laboratory of Digital Finance Innovation（湖北省数字金融创新重点实验室）；School of Information Engineering, Hubei University of Economics（信息工程学院，湖北经济学院）
School of Big-Data Science, Zhejiang University of Science and Technology（浙江科技大学大数据学院）；Network and Information Security Laboratory of Hangzhou Hikvision Digital Technology Co., Ltd.（杭州海康威视数字技术股份有限公司网络与信息安全实验室）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Information and Electronic Engineering, Zhejiang University of Science and Technology（浙江科技大学信息与电子工程学院）
Recent studies have shown convolution neural networks (CNNs) for image recognition are vulnerable to evasion attacks with carefully manipulated adversarial examples. Previous work primarily focused on how to generate adversarial examples closed to source images, by introducing pixel-level perturbations into the whole or specific part of images. In this paper, we propose an evasion attack on CNN classifiers in the context of License Plate Recognition (LPR), which adds predetermined perturbations to specific regions of license plate images, simulating some sort of naturally formed spots (such as…
A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Electrical and Computer Engineering, Kharazmi University（卡扎尔米大学电气与计算机工程学院）；Faculty of Mathematics and Computer Science, University of Bremen（不莱梅大学数学与计算机科学学院）；School of Computing, University of the Fraser Valley（弗雷泽大学计算机学院）
University of Guilan（吉兰大学）；Shahid-Chamran College Technical and Vocational University（沙希德-查姆兰技术与职业大学）；DadeKavan Khazar Pouya company（达德卡万·哈扎尔·普亚公司）
Providing huge amounts of data is a fundamental demand when dealing with Deep Neural Networks (DNNs). Employing these algorithms to solve computer vision problems resulted in the advent of various image datasets to feed the most common visual imagery deep structures, known as Convolutional Neural Networks (CNNs). In this regard, some datasets can be found that contain hundreds or even thousands of images for license plate detection and optical character recognition purposes. However, no publicly available image dataset provides such data for the recognition of Farsi characters used in car lice…
COMSATS University Islamabad（伊斯兰堡COMSATS大学）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（英联邦科学与工业研究组织（CSIRO））；Australian National University (ANU)（澳大利亚国立大学（ANU））；University of Technology Sydney (UTS)（悉尼科技大学（UTS））
We propose an automatic framework for toll collection, consisting of three steps: vehicle type recognition, license plate localization, and reading. However, each of the three steps becomes non-trivial due to image variations caused by several factors. The traditional vehicle decorations on the front cause variations among vehicles of the same type. These decorations make license plate localization and recognition difficult due to severe background clutter and partial occlusions. Likewise, on most vehicles, specifically trucks, the position of the license plate is not consistent. Lastly, for l…
