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License plate detection and recognition (LPDR) has attracted considerable attention in recent years, and many algorithms have presented the competitive performance on several datasets. However, there are still three significant issues to be addressed in this field. Firstly, most methods have poor detection performance in unconstrained scenarios with moving vehicles and highly distracting background objects. Secondly, existing systems generally focus on single image-based algorithms, yet traffic video sequences provide more effective information than individual frames for LPDR tasks. Thirdly, images and videos captured in complex environments may be adversely affected by distortions and low resolution, causing sensitive recognition performance and reduced robustness. To remedy these issues, we propose to automatically perform license plate detection, tracking, and recognition in real-world traffic videos and integrate them into a unified end-to-end framework via deep learning. The contributions of this paper are threefold: 1) A deep flow-guided spatiotemporal license plate detector is proposed to model the video contextual information by introducing optical flow and a novel spatiotemporal attention mechanism; 2) An online license plate tracker is developed to bridge video-based detection and recognition which utilizes both motion and deep appearance information, and innovatively, it can be end-to-end trained with the detector via multi-task learning; 3) The efficient quality-guided license plate recommender and recognizer are proposed to jointly perform stream recognition. The former recommends high-quality frames from video streams while the latter generates recognition results. We evaluate the proposed method on three traffic video-based license plate datasets, and ablation studies have been presented to verify the effectiveness of each component mentioned above. Moreover, extensive experiments are conducted for comparison with other approaches in different scenarios, and the results have demonstrated that our method achieves state-of-the-art performance on all datasets. [ABSTRACT FROM AUTHOR]

• A new problem of license plate/text detection in drone images is addressed. • The concept of Phase congruency in DCT domain is introduced for detection. • The combination of DCT and Phase congruency is new. • Deep learning is used in different way for eliminating false positives. • The combination of feature extraction and DCT-Phase is new. License plate number detection in drone images is a complex problem because the images are generally captured at oblique angles and pose several challenges like perspective distortion, non-uniform illumination effect, degradations, blur, occlusion, loss of visibility etc. Unlike, most existing methods that focus on images captured by orthogonal direction (head-on), the proposed work focuses on drone text images. Inspired by the Phase Congruency Model (PCM), which is invariant to non-uniform illuminations, contrast variations, geometric transformation and to some extent to distortion, we explore the combination of DCT and PCM (DCT-PCM) for detecting license plate number text in drone images. Motivated by the strong discriminative power of deep learning models, the proposed method exploits fully connected neural networks for eliminating false positives to achieve better detection results. Furthermore, the proposed work constructs working model that fits for real environment. To evaluate the proposed method, we use our own dataset captured by drones and benchmark license plate datasets, namely, Medialab for experimentation. We also demonstrate the effectiveness of the proposed method on benchmark natural scene text detection datasets, namely, SVT, MSRA-TD-500, ICDAR 2017 MLT and Total-Text. [ABSTRACT FROM AUTHOR]

License plate detection is the first and essential step of the license plate recognition system and is still challenging in real applications, such as on-road scenarios. In particular, small-sized and multi-oriented license plates, mainly caused by the remote and mobile camera, are challenging to detect. We propose a novel and applicable method for degraded license plate detection via vehicle-plate relation mining in this work. The proposed method can detect the license plate in a coarse-to-fine scheme. First, we propose to estimate the plate by using the relationships between the vehicle and the license plate, which can significantly reduce the search area and precisely detect small-sized license plates. Second, we present to robustly detect the multi-oriented license plate by regressing the four corners of the license plate in the local region. The whole network is constructed in an end-to-end manner, and codes are available at https://github.com/chensonglu/LPD-end-to-end. [ABSTRACT FROM AUTHOR]

With the continuous development of waterway trade, intelligent waterway supervision is gradually becoming an important way to maintain waterway safety. In this field, ship license plate (SLP) detection is importance for intelligent waterway supervision. However, in practical surveillance scenarios, detecting ship license plates (SLPs) is challenging because of small size, and text areas in complex backgrounds can also be mistakenly identified as SLPs. To solve these problems, we propose a Target Occlusion Contrast Network (TOCNet) for SLP detection. Firstly, to mitigate the effects of complex backgrounds, a novel Target Occlusion Contrast Learning (TOCL) approach is proposed, which is used to widen the decision boundary of the model and reduce false alarms. Secondly, to solve the problem that small targets are difficult to be detected, we design a Similarity Fusion Module (SFM), which effectively improves the detection performance of the model for small targets by similarity weight. Finally, to further improve the localization accuracy, we propose the task-oriented decoupled head (TDH), which facilitates the localization accuracy through selective fusion. Experimental results on both the SLPD dataset and the UFPR-ALPR dataset show that our proposed modules effectively improve the detection performance of the model, and the proposed TOCNet model exhibits a better detection performance than the other existing state-of-the-art (SOTA) models. Our code and dataset are publicly available at https://github.com/ssyuyi/tocnet. • The proposed contrast learning reduces the false alarm rate of the detection model. • The proposed similarity fusion module facilitates the detection for small targets. • A task-oriented decoupling head is proposed to facilitate detection accuracy. • A dataset that can be used for ship license plate detection is provided. • The proposed TOCNet model exhibits the performance of SOTA. [ABSTRACT FROM AUTHOR]

Number Plate recognition, also called License Plate realization or recognition using image processing methods is a potential research area in smart cities and Internet of Things. Many of the existing automated number plate recognition systems work only in a controlled environment where images are captured from a straight angle with good illumination, clarity and standard fonts. Another drawback of existing works is that, they are based on UK number plates and may not suite for Indian number plates. This paper presents a novel image processing system for Indian number plate detection and recognition that can deal with, noisy, low illuminated, cross angled, non-standard font number plates. This work employs several image processing techniques such as, morphological transformation, Gaussian smoothing, and Gaussian thresholding in the pre-processing stage. Next, for number plate segmentation, contours are applied by border following and contours are filtered based on character dimensions and spatial localization. Finally, after the region of interest filtering and de-skewing, K-nearest neighbor algorithm is used for character recognition. The proposed methods demonstrated promising results. [ABSTRACT FROM AUTHOR]

• License plate detection for multi-national vehicles in a multi-lane scenario. • Ability to deal with multi-national license plates of various colors and styles. • Capability to handle illumination variance. • Performance evaluation on datasets of China, Thailand, Pakistan, USA, UAE and Netherlands. • Superior performance as compared to conventional and deep learning approaches. Only a few methods in literature are effective for multi-national license plate detection in a multi-lane scenario. These methods are prone to illumination variance, complex background and weak-edged license plates. In this paper, we propose a novel illumination invariant method to handle multi-national vehicle license plates of different colors and styles. Red corona is initially used to detect the tail-lights of vehicles to establish region-of-interest as the license plates are in a vicinity of its tail-lights. The vertical edges within each region-of-interest are obtained using a unique approach that preserve license plate edges for improved performance. Heuristic energy map is then used to distinguish the license plate area. To validate the detected regions, high-level features extracted from AlexNet Convolutional Neural Network are used. Extensive experiments on the license plates of six countries show that the proposed approach not only ensures real-time performance, but also outperforms the conventional and deep-learning methods. [ABSTRACT FROM AUTHOR]

Abstract 1 1 HSI: hue-saturation-intensity; 2D: two-dimensional; 3D: three-dimensional; REG: special register. This paper presents a new algorithm for detecting and extracting license plates from a complex image by using a fuzzy two-dimensional Gabor filter. The filter parameters are fuzzified to optimize the Gabor filter. In particular, the orientation and wavelengths of the Gabor filter are fuzzified. Fuzzification of the wavelength results in greater selectivity. These parameters dominate the filtering results. Bell’s function and triangular membership function were proven to be the most efficient method for selecting the filter parameters in our experiments and were used for fuzzification. The use of these filters provided satisfactory results; the components of interest were efficiently extracted, and the procedure was found to be very noise-resistant. [ABSTRACT FROM AUTHOR]

License Plate Recognition (LPR) system plays a very important role in intelligent traffic control and security management, such as parking enforcement, stolen vehicle recovery and surveillance operations. This research describes a new method for the number recognition of Tunisian license plate. Based on low pass filter in license plate extraction module and network in LPR, the proposed method shows a great robustness in most unfavorable conditions. [ABSTRACT FROM AUTHOR]

Abstract: Adaboost detector has been successfully used in object detection. In this paper, we propose a new License Plate (LP) detection technique based on multistage information fusion, which is adopted to reduce high false alarm rate in the conventional Adaboost detector. The proposed multistage information fusion system is composed of an enhanced Adaboost detector, a color checking module and an SVM detector, where the latter two stages further check whether the image patch that gets through the Adaboost detector is an LP. Test results of the dataset that consists of 950 real-world images show that the fusion reduces the false alarm rate. The proposed Fusion detector outperforms the conventional Adaboost detector throughout the ROC (Receiver Operating Characteristic) curve. The AUC (Area Under the Curve) of the best Fusion detector reaches 0.9081; however, the AUC of the best Adaboost detector is only 0.8441, which shows that the modification on feature extraction and the multistage information fusion significantly improve the LP detection performance. [Copyright &y& Elsevier]

Abstract: In this paper, we present a novel method to detect license plates in video sequences automatically. The framework mainly integrates the cascade detectors method and the Tracking Learning Detection (TLD) algorithm. The cascade detectors are used to detect license plates, and the TLD algorithm is adopted to track the license plate regions. The license plates in the first frame image are detected by the cascade detectors to build the original tracking list, the tracking results and the detection results in following frames will be compared, and the newly appearing license plate information will be added to the tracking list. Meanwhile, the tracking results existing in the current tracking list would be replaced by the corresponding detection results with higher degree of confidence. We demonstrate the effectiveness of our algorithm for license plate detection task on a road intersection dataset, and the experimental evaluation shows the detection performance has been greatly improved by synthetically using the detection and tracking strategy. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a license plate detection method based on the gradient information and a cascade detection framework. The proposed method consists of three main modules: image preprocessing, license plate detection and license plate confirmation. Considering the diversities of Chinese license plate, we first get the gradient images to unify plate forms through image preprocessing. The license plate will be detected roughly by using the cascade AdaBoost classifier in the next step. Finally, several heuristic judgment strategies and a voting-based method are used to verify the candidate license plates. The effectiveness of our method is validated with an experimental study in license plate detection. [Copyright &y& Elsevier]

License plate recognition is an emerging topic for real-time applications in smart city development because of automatic systems for toll fee-paying, traffic controlling, and vehicle detection. Speed of vehicles, unpredictable weather conditions such as night/low light/limited light images, and capturing images at different angles make recognition harder. This paper presents a new Fractal Series Expansion (FSE) model for license plate image enhancement. The proposed FSE model is justified because it estimates the high probability for the pixels which represent the license plate compared to the pixels which represent background irrespective of the above challenges, resulting in enhanced image. Besides, since the FSE model considers local information for estimating probability, the model has the ability to tackle non-uniform degradations as well as distortion affected multiple adverse factors. In addition to qualitative results, to validate the effectiveness of the proposed enhancement, quantitatively, recognition rates of the different methods before and after enhancement are computed. For this purpose, we have considered different datasets like dataset of Night License Plate Images (NLPI), which consists of images captured in the night and low lights environment, the UCSD benchmark dataset which provides poor and high quality day license plate images, etc. It is noted that recognition results after enhancement is higher than that of before enhancement, and hence our enhancement is useful and effective. • A new Fractal Series Expansion model for license plate image enhancement. • It tackles non-uniform degradations and distortions. • Different datasets including night license images are validated. [ABSTRACT FROM AUTHOR]