Real-Time Vehicle License Plate Recognition Using Deep Learning

Fu, Meixia; Chen, Na; Hou, Xiaoying; Sun, Heng; Abdussalam, Amr; Sun, Songlin

doi:10.1007/978-981-13-1733-0_5

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…Each of the four softmax layers is responsible for recognizing one corresponding character of the four-character CAPTCHA image. The structure of this multilabel-CNN model is similar to that of model-5 CNN proposed in [36]. The Adam optimizer with a learning rate of 0.00001 is used to optimize the cross-entropy loss functions of this CNN.…”

Section: Comparison Resultsmentioning

confidence: 99%

“…The feature extraction part of the proposed CRABI-CNN is based on the Model-5 architecture introduced in [36]. The CRABI-CNN architecture consists of 17 convolutional layers, 5 maxpooling layers, 1 flatten layer, 1 dropout layer, and 1 output softmax layer and their parameters are set to accelerate the training process and improve features extraction.…”

Section: Structure and Parameters Of The Proposed Crabi-cnnmentioning

confidence: 99%

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CAPTCHA Recognition Using Deep Learning with Attached Binary Images

et al. 2020

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Websites can increase their security and prevent harmful Internet attacks by providing CAPTCHA verification for determining whether end-user is a human or a robot. Text-based CAPTCHA is the most common and designed to be easily recognized by humans and difficult to identify by machines or robots. However, with the dramatic advancements in deep learning, it becomes much easier to build convolutional neural network (CNN) models that can efficiently recognize text-based CAPTCHAs. In this study, we introduce an efficient CNN model that uses attached binary images to recognize CAPTCHAs. By making a specific number of copies of the input CAPTCHA image equal to the number of characters in that input CAPTCHA image and attaching distinct binary images to each copy, we build a new CNN model that can recognize CAPTCHAs effectively. The model has a simple structure and small storage size and does not require the segmentation of CAPTCHAs into individual characters. After training and testing the proposed CAPTCHA recognition CNN model, the achieved experimental results reveal the strength of the model in CAPTCHA character recognition.

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Section: Comparison Resultsmentioning

confidence: 99%

Section: Structure and Parameters Of The Proposed Crabi-cnnmentioning

confidence: 99%

CAPTCHA Recognition Using Deep Learning with Attached Binary Images

et al. 2020

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“…x x (11) where, m i x is the WO parameters which is chosen optimally, teams P defines population of all continent, M is the value of the continents, and…”

Section: Start Initialization: Population (X I ) a C And A Assess The...mentioning

confidence: 99%

“…In 2018, Fu et al presented a plate detection system for Chinese vehicles based on Deep Learning [11]. Their method contained two parts of localizing the license plate and detecting the car plate characters based on leveraging CNN without segmentation.…”

Section: Introductionmentioning

confidence: 99%

2022.1.2

2022

Journal of Smart Systems and Stable Energy

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One of the important and more challenging categories in the smart cities and IoT is to monitor the vehicles plate licenses. This system is a key factor in most of the traffic monitoring in the IoT based smart city applications. In this research, a method for plate license recognition based on optimal training of the CNN is proposed. To do this, the configuration and the hyperparameters of the CNN were optimized by a new hybrid optimization including world cup optimizer, whale optimizer, and chaotic theory to obtain a better result with high convergence. Simulations are applied to the UFPR-ALPR dataset and are compared with six popular techniques in terms of accuracy and time. Experimental achievements indicated that the proposed method gives superiority toward the other comparative techniques and is an efficient method for vehicles plate licenses detection.

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“…[27] proposed a transfer learning-based technique which significantly minimized the complexity of an attack and its associated cost of labeling samples. [28] investigated few contemporary networks that presented an effect of the convolutional kernel depth, size, and width of CNN. [29] focused on recognition/detection of Chinese car LP in complex background.…”

Section: Introductionmentioning

confidence: 99%

Your CAPTCHA Recognition Method Based on DEEP Learning Using MSER Descriptor

Kumar¹,

Singh²,

Bamber³

2022

Computers, Materials &Amp; Continua

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Individuals and PCs (personal computers) can be recognized using CAPTCHAs (Completely Automated Public Turing test to distinguish Computers and Humans) which are mechanized for distinguishing them. Further, CAPTCHAs are intended to be solved by the people, but are unsolvable by the machines. As a result, using Convolutional Neural Networks (CNNs) these tests can similarly be unraveled. Moreover, the CNNs quality depends majorly on: the size of preparation set and the information that the classifier is found out on. Next, it is almost unmanageable to handle issue with CNNs. A new method of detecting CAPTCHA has been proposed, which simultaneously solves the challenges like preprocessing of images, proper segmentation of CAPTCHA using strokes, and the data training. The hyper parameters such as: Recall, Precision, Accuracy, Execution time, F-Measure (H-mean) and Error Rate are used for computation and comparison. In preprocessing, image enhancement and binarization are performed based on the stroke region of the CAPTCHA. The key points of these areas are based on the SURF feature. The exploratory outcomes show that the model has a decent acknowledgment impact on CAPTCHA with foundation commotion and character grip bending.

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Real-Time Vehicle License Plate Recognition Using Deep Learning

Cited by 5 publications

References 9 publications

CAPTCHA Recognition Using Deep Learning with Attached Binary Images

CAPTCHA Recognition Using Deep Learning with Attached Binary Images

2022.1.2

Your CAPTCHA Recognition Method Based on DEEP Learning Using MSER Descriptor

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