Localization Techniques in ANPR Systems: A-State-of-Art

Rana, Neha; Dahiya, Pawan Kumar

doi:10.23956/ijarcsse/sv7i5/0338

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Consequently, it was used with template matching technique to analyze every character in LP image. Rana et al [9] defined numerous detection approaches for LP and related the function on same metrics. Further, this technique was applied with signature analysis along with CCA, and Euclidean distance transformation.…”

Section: A Lp Detection Techniquesmentioning

confidence: 99%

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

et al. 2020

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Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.

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Section: A Lp Detection Techniquesmentioning

confidence: 99%

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

et al. 2020

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“…N. Rana et al has discussed several detection techniques for license plate and compare their performance on similar parameters [12]. They have used signature analysis with Connected component analysis, and Euclidean distance transform).…”

Section: Related Workmentioning

confidence: 99%

Toward an Optimized Neutrosophic k-Means With Genetic Algorithm for Automatic Vehicle License Plate Recognition (ONKM-AVLPR)

et al. 2020

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The present paper proposes a new methodology for license plate (LP) recognition in the state of the art of image processing algorithms and an optimized neutrosophic set (NS) based on genetic algorithm (GA). First of all, we have performed some image processing techniques such as edge detection and morphological operations in order to utilize the (LP) localization. In addition, we have extracted the most salient features by implementing a new methodology using (GA) for optimizing the (NS) operations. The use of (NS) decreases the indeterminacy on the (LP) images. Moreover, k-means clustering algorithm has been applied to segment the (LP) characters. Finally, we have applied connected components labeling analysis (CCLA) algorithm for identifying the connected pixel regions and grouping the appropriate pixels into components to extract each character effectively. Several performance indices have been calculated in order to measure the system efficiency such as accuracy, sensitivity, specificity, dice, and jaccard coefficients. Moreover, we have created a database for all detected and recognized (LP) for testing purposes. Experimental results show that the proposed methodology has the ability to be suitable for both (Arabic-Egyptian) and English (LP). The proposed system achieves high degree of recognition accuracy for the whole system according to the following case studies; (i) for a high resolution Egyptian (LP), the proposed system achieves about 96.67% accuracy of correct recognition, (ii) for a low resolution-corrupted English (LP), the proposed system achieves about 94.27% accuracy. In addition, we have applied the proposed system on some sort of image disturbance i.e. (flash in image, external noise, and illumination variation), the proposed system achieves about 92.5% accuracy of correct identification. However, traditional methods achieve about 79% accuracy of correct identification in the presence of such image degradations. This reflects how the proposed system is generalized, optimized, and proposes high degree of recognition accuracy.

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“…Finally, candidates are verified using the geometrical and textural properties. To improve the efficiency of the algorithm the concept of integral image was adopted.Comparison of various techniques is done below along with this technique represented in this paper [14]. …”

Section: Sliding Window Techniquementioning

confidence: 99%

License Plate Localization Method Based on VerticalEdge Detection

Rana

Dahiya

2017

IJARCSSE

Self Cite

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In our examination, a powerful algorithm for plate recognition which is able to recognize plates having different dimensions in unaccustomed and complicated backgrounds.License plate detection is first step in ANPR systems. The paper defines an algorithm to detect license plate.The plate is detected based on its edge density features. Integral edge image is calculated and then edge density conditions are verified.Two stage detection process is used and provides 97% accuracy.

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Localization Techniques in ANPR Systems: A-State-of-Art

Cited by 7 publications

References 5 publications

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

Toward an Optimized Neutrosophic k-Means With Genetic Algorithm for Automatic Vehicle License Plate Recognition (ONKM-AVLPR)

License Plate Localization Method Based on VerticalEdge Detection

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