License plate recognition based on edge histogram analysis and classifier ensemble

Nejati, Mansour; Majidi, Ali; Jalalat, Morteza

doi:10.1109/spis.2015.7422310

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…Then, using the Sobel operator and the morphological operation, the place of the plate in the changing climate conditions, distance, brightness and rotation, but there is no way to improve the rotation and precision of the cursor. In [4], in order to expedite the operation, first, the size of the image is considered small, then Gaussian noise reduction filters and histogram homogenization are applied to determine the range of the plate using the representation of the vertical edges and its analysis. In [3], to identify and find out the location, the linear gradient correction and the Sobel operator are used to reveal the vertical edges, and then connect the edges with the appropriate morphological operators.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Regarding Figure 1, we can classify vehicle license locations in four general categories. Much of the work done in the literature review [3][4][5][6][7][8][9][10][11][12][13][14][15][16] department at least in one part of their work uses edge-based methods, morphological operations, and portrait and landscape image histograms. The second category involves the use of image-based algorithms [6,14,17,19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Effective and Fast Iranian License Plate Detection Using Statistical and Geometrical Approaches

Poursiyah¹,

Salami²,

Mohebbi³

et al. 2018

Adv. Sci. Technol. Res. J.

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The first step in the process of detection a license plate is to determine the location of the plate. The output of this stage should be accurate enough and calculations will be completed within a short time. The reason for this is that the output of this stage is as input in the next steps. If the step to determine the location is encountered error, then the operation of the next steps will also be interrupted. In this paper, a new method is used to improve the contrast of the image, delete non-numeric characters, analysis and clustering of plain characters in order to determine the location of an Iranian car license plate with dark characters, a clear background is provided. The proposed method reduces the overall complexity of the algorithm and in addition to its ease of implementation, the system's speed and efficiency improve the location of the plate. The proposed algorithm is independent of the number of vehicle plates in the image, image size, complete unread plate and in contrast to brightness variations, it is largely resistant. The results of the test on two different data sets with 67 and 492 images, to an accuracy of 100 and 99.59 percent with an error rate of 1.5 and 1.63 and the runtime of 109 and 17.5 milliseconds averaged, were achieved.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Effective and Fast Iranian License Plate Detection Using Statistical and Geometrical Approaches

Poursiyah¹,

Salami²,

Mohebbi³

et al. 2018

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

show abstract

“…Further, rain drops increase unrelated edges and the low contrast of the image also decreases the detection of sharp edges in the image; this misleads the clustering method and reduces the detection performance of the license plate areas. Nejati et al [22] have www.ijacsa.thesai.org presented a license plate detection system. Their proposed algorithm consists of four main steps including: license plate positioning, segmentation, identification and a combination of the results of multiple identifications.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Method for Detecting the Shaded Images of the Car License Plates based on Histogram Equalization and Probabilities

Faghedi¹,

Barekatain²,

Raahemifar³

2018

ijacsa

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Shadow is one of the major and significant challenges in detection algorithms which track the objects such as the license plates. The quality of images captured by cameras is influenced by weather conditions, low ambient light and low resolution of the camera. The shadow in images reduces the reliability of the sight algorithms of the device as well as the visual quality of images. The previous papers indicate that no effective method has been presented to improve the license plate detection accuracy of the shaded images. In other words, the methods that have been presented for automatic license plate detection in shadowed images until now use a combination of color features and texture of the image. In all these methods, in order to detect the frame of the shadow and the texture of the image, sufficient light is required in the image; this necessity cannot be found in most of the regular images captured by road cameras. In order to solve this problem, an improved license plate detection method is presented in this research which is able to detect the license plate area in shadowed images effectively. In fact, this is a contrast-improving method which utilizes the dual binary method for automatic plate detection and is introduced to analyze the interior images with low contrast, and also night shots, blurred and shadowed images. In this method, the histogram of the image is firstly calculated for each dimension and then the probability of each pixel in the whole image is obtained. As a result, after calculating the cumulative distribution of the pixels and replacing it in the image, it will be possible to remove the shadow from the image easily. This new method of detection was tested and simulated for 1000 images of vehicles under different conditions. The results indicated the detection accuracy of 90/30, 97/87 and 98/70 percent for the license plates detection in three databases of University of Zagreb, Numberplates.com and National Technical University of Athens, respectively. In other words, comparing the performance of the proposed method with two similar and new methods, namely Hommos and Azam, indicates an average improvement of 26/70 and 72/95 percent for the plate detection and 32/38 and 36/53 percent for the time required for rapid and correct license plate detection, even in shaded images.

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“…A recent research carried out by Nejati et al [9], proposed a new approach for localization of LP. In this method, similar to Safaei et al [2] approach, down-sampling is the first step of the processing.…”

Section: Related Workmentioning

confidence: 99%

“…Then, to detect the candidate frame edges which are expected to contain the LP, the histogram of vertical edges followed by filtering is used to reduce the number of false detections. In the last step of the method by Nejati et al [9] (the localization phase), the candidate plates are localized using the aspect ratio and the vertical projection of the histogram of edges. Although this approach has a reasonable accuracy of 95%, its speed is not suitable for ITS applications.…”

Section: Related Workmentioning

confidence: 99%

Incorporating negentropy in saliency-based search free car number plate localization

Safaei

Tang

Sanei

2016

2016 IEEE International Conference on Digital Signal Processing (DSP)

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License plate localization algorithms aim to detect license plates within the scene. In this paper, a new algorithm is discussed where the necessary conditions are imposed into the saliency detection equations. Measures of distance between probability distributions such as negentropy finds the candidate license plates in the image and the Bayesian methodology exploits the a priori information to estimate the highest probability for each candidate. The proposed algorithm has been tested for three datasets, consisting of gray-scale and color images. A detection accuracy of 96% and an average execution time of 80 ms for the first dataset are the marked outcomes. The proposed method outperforms most of the state-of-the-art techniques and it is suitable to use in real-time ALPR applications.

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License plate recognition based on edge histogram analysis and classifier ensemble

Cited by 15 publications

References 9 publications

An Effective and Fast Iranian License Plate Detection Using Statistical and Geometrical Approaches

An Effective and Fast Iranian License Plate Detection Using Statistical and Geometrical Approaches

An Enhanced Method for Detecting the Shaded Images of the Car License Plates based on Histogram Equalization and Probabilities

Incorporating negentropy in saliency-based search free car number plate localization

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