13,610 research outputs found
License plate localization based on statistical measures of license plate features
— License plate localization is considered as the most important part of license
plate recognition system. The high accuracy rate of license plate recognition is depended on
the ability of license plate detection. This paper presents a novel method for license plate
localization bases on license plate features. This proposed method consists of two main
processes. First, candidate regions extraction step, Sobel operator is applied to obtain
vertical edges and then potential candidate regions are extracted by deploying mathematical
morphology operations [5]. Last, license plate verification step, this step employs the
standard deviation of license plate features to confirm license plate position. The
experimental results show that the proposed method can achieve high quality license plate
localization results with high accuracy rate of 98.26 %
Recognizing License Plates in Real-Time
License plate detection and recognition (LPDR) is of growing importance for
enabling intelligent transportation and ensuring the security and safety of the
cities. However, LPDR faces a big challenge in a practical environment. The
license plates can have extremely diverse sizes, fonts and colors, and the
plate images are usually of poor quality caused by skewed capturing angles,
uneven lighting, occlusion, and blurring. In applications such as surveillance,
it often requires fast processing. To enable real-time and accurate license
plate recognition, in this work, we propose a set of techniques: 1) a contour
reconstruction method along with edge-detection to quickly detect the candidate
plates; 2) a simple zero-one-alternation scheme to effectively remove the fake
top and bottom borders around plates to facilitate more accurate segmentation
of characters on plates; 3) a set of techniques to augment the training data,
incorporate SIFT features into the CNN network, and exploit transfer learning
to obtain the initial parameters for more effective training; and 4) a
two-phase verification procedure to determine the correct plate at low cost, a
statistical filtering in the plate detection stage to quickly remove unwanted
candidates, and the accurate CR results after the CR process to perform further
plate verification without additional processing. We implement a complete LPDR
system based on our algorithms. The experimental results demonstrate that our
system can accurately recognize license plate in real-time. Additionally, it
works robustly under various levels of illumination and noise, and in the
presence of car movement. Compared to peer schemes, our system is not only
among the most accurate ones but is also the fastest, and can be easily applied
to other scenarios.Comment: License Plate Detection and Recognition, Computer Vision, Supervised
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AON: Towards Arbitrarily-Oriented Text Recognition
Recognizing text from natural images is a hot research topic in computer
vision due to its various applications. Despite the enduring research of
several decades on optical character recognition (OCR), recognizing texts from
natural images is still a challenging task. This is because scene texts are
often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted)
arrangements, which have not yet been well addressed in the literature.
Existing methods on text recognition mainly work with regular (horizontal and
frontal) texts and cannot be trivially generalized to handle irregular texts.
In this paper, we develop the arbitrary orientation network (AON) to directly
capture the deep features of irregular texts, which are combined into an
attention-based decoder to generate character sequence. The whole network can
be trained end-to-end by using only images and word-level annotations.
Extensive experiments on various benchmarks, including the CUTE80,
SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed
AON-based method achieves the-state-of-the-art performance in irregular
datasets, and is comparable to major existing methods in regular datasets.Comment: Accepted by CVPR201
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Robust search-free car number plate localization incorporating hierarchical saliency
There are two major shortcomings associated with presently implemented automatic license plate recognition (ALPR) systems: first, processing images with complex background is time-consuming and second, the results are not sufficiently accurate. To overcome these problems and also to achieve a robust recognition of multiple car number plates, saliency detection based on the ALPR system is used in this paper and also an improved and more effective definition of saliency is presented. In this new approach, the notion of the directionality of the edges using Gabor filtering and the detection of the patterns of numbers using L1 -norm have been added to the traditional saliency detection method. The proposed algorithm was tested on 660 images; some consisting of two or more cars.
A detection accuracy of 94.77% and an average execution time of 40 ms for 600 Ă— 800 images are the marked outcomes. The proposed SB-ALPR method outperforms most of the state of the art techniques in terms of execution time and accuracy, and can be used in real-time applications. Also, unlike some recently introduced saliency-based ALPR methods, our two-stage saliency detection approach exploits smaller numbers of sample sizes to reduce the computation cost
Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios
Benefiting from the rapid development of convolutional neural networks, the
performance of car license plate detection and recognition has been largely
improved. Nonetheless, challenges still exist especially for real-world
applications. In this paper, we present an efficient and accurate framework to
solve the license plate detection and recognition tasks simultaneously. It is a
lightweight and unified deep neural network, that can be optimized end-to-end
and work in real-time. Specifically, for unconstrained scenarios, an
anchor-free method is adopted to efficiently detect the bounding box and four
corners of a license plate, which are used to extract and rectify the target
region features. Then, a novel convolutional neural network branch is designed
to further extract features of characters without segmentation. Finally,
recognition task is treated as sequence labelling problems, which are solved by
Connectionist Temporal Classification (CTC) directly. Several public datasets
including images collected from different scenarios under various conditions
are chosen for evaluation. A large number of experiments indicate that the
proposed method significantly outperforms the previous state-of-the-art methods
in both speed and precision
Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition
Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve the effectiveness of ALPR systems in foggy, low-light, and rainy weather conditions using a hybrid preprocessing methodology. The research proposes the combination of dark channel prior (DCP), non-local means denoising (NMD) technique, and adaptive histogram equalization (AHE) algorithms in CIELAB color space. And used the Python programming language comparisons for SSIM and PSNR performance. The results showed that this hybrid approach is not merely robust to a variety of challenging conditions, including challenging weather and lighting conditions but significantly more accurate for existing ALPR systems
A Support Vector Machine (SVM) and Speeded Up Robust Features (SURF) for Indonesian Car Licence Plate Identification System
Volume 7 Issue 10 (October 201
Vision-based Detection of Mobile Device Use While Driving
The aim of this study was to explore the feasibility of an automatic vision-based solution to detect drivers using mobile devices while operating their vehicles. The proposed system comprises of modules for vehicle license plate localisation, driver’s face detection and mobile phone interaction. The system were then implemented and systematically evaluated using suitable image datasets. The strengths and weaknesses of individual modules were analysed and further recommendations made to improve the overall system’s performance
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