Automatic car number plate recognition

Sasi, Anumol; Sharma, Swapnil; Cheeran, Alice N.

doi:10.1109/iciiecs.2017.8275893

Cited by 25 publications

(16 citation statements)

References 13 publications

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“…They are either using a combination of multiple classifiers in parallel or have multistage classification schemes. Artificial Neural Network is used in [ 126 ]. Convolutional Neural Networks is used in [ 78 ] in real time scenario and has shown great results for each stage of ANPR system.…”

Section: Discussionmentioning

confidence: 99%

“…The alternate method of feature extraction is also used for Character Recognition; it reduces processing time and eliminates the pixels of less importance [ 97 ]. SVM is used for character recognition in [ 126 , 127 ]. For challenging and complicated number plates, an improved SVM based algorithm is proposed in [ 128 ].…”

Section: Number Plate Recognition Methodsmentioning

confidence: 99%

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Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.

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Section: Discussionmentioning

confidence: 99%

Section: Number Plate Recognition Methodsmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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“…They also covered the segmentation of different types of conglutination. Chellapilla and Simard [23] used the connected component algorithm [24,25] to segment several CAPTCHA schemes, including Yahoo and Google, and achieved a success rate between 4.89% and 66.2%. However, vertical projection and connected component algorithms involve numerous preprocessing operations that are computationally expensive and time consuming.…”

Section: Related Workmentioning

confidence: 99%

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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“…The two-stage character segmentation approach [8] is proposed for Chinese LPs by using CCA and bank of harrow-shaped filter (HSF). The CCA and kohonen neural network [9] is used to segment characters from Indian LPs. The CCA and Contour detection is used for character segmentation in Bangladesh and Iraq license plates [10][11][12].…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

Asif

Khan

2020

Applied Sciences

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License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.

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Automatic car number plate recognition

Cited by 25 publications

References 13 publications

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

CAPTCHA Recognition Using Deep Learning with Attached Binary Images

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

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