Peak position recognizing characters in Saudi license plates

Mellouli, Khaled; Zantout, Rached; Obeid, Hasan R.

doi:10.1109/ieeegcc.2011.5752479

Cited by 12 publications

(8 citation statements)

References 1 publication

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“…Results of the segmentation and recognition method are shown in figures (7)(8)(9)(10) Plate after rotation BW Plate after rotation The proposed system locates and recognizes license plates under complex scenarios like variation of illumination, angle, and scale. Good results have been obtained.…”

Section: Resultsmentioning

confidence: 99%

Saudi License Plate Recognition

Basalamah¹

2013

IJCEE

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Abstract-Automatic license plate recognition systems (LPR) can help reduce the number of traffic violations and make our streets safer. In this project we developed an automatic system that locates Saudi license plates in a captured image regardless of the time of day or license plate scale. The proposed system can tolerate slight tilting of the license plate. The localization process is fairly complex due to the highly varying nature of the background. Good results were obtained using the localization stage. A second part of the system was developed to segment and recognize the characters in the located license plate. Khalil [5] were all on the previous Saudi license plates series. Index Terms-IntelligentMaglad [6], [7] proposed a system that uses radial basis function neural networks for the detection and recognition of plates. Khalid Almustafa [8]- [10] proposed an algorithm that uses pixel density to recognize characters in license plates. Shaaban [11] proposed a system where a neural network classifier is used to recognize the characters based on edge moment invariants and principal component analysis features of wavelet coefficient matrix.Alginahi [12] proposed a system that recognized both old and new Saudi license plates. The license plates are detected by searching the vertical lines then the width to length ratio is used to locate the plate. Character segmentation is then performed before both a distance classifier and a Neural Network classifier are used to recognize characters.Deriche [13] proposed a hybrid system of RFID and license plate recognition. The license plate detection is based on morphological filtering and the character recognition is based on neural networks. Mansour [14] proposed a system based on chain code and tested it on both Egyptian and Saudi license plates. decade. Figure (1 1950, 1962, 1972, 1978, 1996 and 2007. III. PROPOSED SYSTEM II. SAUDI LICENSE PLATES A. LocalizationLocating the license plate in an image is the first step in any license plate recognition system. Variation in license plate location in different cars, variation in location of cars in images and variation in lighting during day/night makes this step challenging.We started looking into locating the plates by searching for black lines that borders the license plates thus we have to look for a horizontal line and a vertical line, however we International Journal of Computer and Electrical Engineering, Vol. 5, No. 1, February 2013 found that in many cars that border is hidden under a frame that is commonly used to hold the plate.We have noticed that particular to Saudi license plates, there is a black cross that centers all license plates, this cross is visible in all plates and is not hidden by frames since is it is in the center of the license plates.We apply an edge detector to find the horizontal and vertical maps; we then use the average value of pixels in each map as a threshold to convert it into a binary image and use a median filter to remove noise and find the binary addition of the maps. To h...

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Section: Resultsmentioning

confidence: 99%

Saudi License Plate Recognition

Basalamah¹

2013

IJCEE

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“…Similarly, lines R, V and L are three vertical lines drawn across the image at distances f of the total width from the image right boundary, at the middle and f of the total width from the image left boundary respectively. A Peak is defined as a crossing from a black pixel to a white pixel [21] as the image is scanned from right to left (for lines T, H and B) or top to bottom (for lines R, V and L). A line starting with white pixels is considered to have one peak at the beginning.…”

Section: The Number Of Peaks Algorithmmentioning

confidence: 99%

Recognition of triangular traffic signs using the Number of Peaks algorithm

Hamandi

Mellouli

Zantout

et al. 2012

2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)

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Automatic detection and recognition of traffic signs is an important tool in intelligent vehicles. It allows more autonomous vehicles and it can alert the driver to possible hazards and changes in the road. In this paper we focus on the recognition of a wide set of triangular traffic signs using a novel algorithm, the Number of Peaks. Once a traffic sign is detected, three horizontal lines (T, H, B) and three vertical lines (R, V, L) across the image are used to recognize the sign. The number of crossings from a black pixel to a white pixel (peak) on each line is calculated. A simple and fast decisiontree-like search algorithm uses the number of peaks to differentiate between the triangular road signs. A 100% correct detection rate is achievable even in a fairly noisy environment.

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“…We have two types of crossing lines for this algorithm as seen in Figure 17, the solid lines with titles adjacent to them as H, T, B, V, L and R, and are associated with the factor F. The other crossing lines are the dashed lines and are fixed at the 1/3 and 2/3 locations for the horizontal and the vertical lines [21]. The image is divided into nine different locations.…”

Section: Position Of the Peaks Algorithmmentioning

confidence: 99%

“…In this algorithm [21], the positions of the peaks for a given crossing line are considered for a given factor. We have two types of crossing lines for this algorithm as seen in Figure 17, the solid lines with titles adjacent to them as H, T, B, V, L and R, and are associated with the factor F. The other crossing lines are the dashed lines and are fixed at the 1/3 and 2/3 locations for the horizontal and the vertical lines [21].…”

Section: Position Of the Peaks Algorithmmentioning

confidence: 99%

On the Automatic Recognition of Saudi License Plate

Mellouli¹

2013

IJAIS

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In this paper three different algorithms for Automatic License Plate Recognition (ALPR) of the Saudi License Plates are described. All algorithms rely on processing information from lines strategically drawn vertically and horizontally through a character. The first algorithm calculates the number of peaks for each line. A peak is a place in the line where the pixels change from black to white. The second algorithm calculates the pixels density for a specific crossing line in a character. Pixel density is defined as the number of pixels having a specific intensity level to the total number of pixels in a line. The third algorithm calculates the position of the peaks introduced in the first algorithm rather than only their numbers. An algorithm was developed for each method to differentiate between all characters of the license plate. Uniformly distributed pseudo-random noise was added to simulate the performance of these algorithms in the presence of noisy images, also performance of the suggested algorithms were tested due to image rotation. A comparison between these algorithms also presented. These algorithms were proven to work even in some cases in which the characters were extremely degraded by noise.

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Peak position recognizing characters in Saudi license plates

Cited by 12 publications

References 1 publication

Saudi License Plate Recognition

Saudi License Plate Recognition

Recognition of triangular traffic signs using the Number of Peaks algorithm

On the Automatic Recognition of Saudi License Plate

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