Machine Vision and Applications (2010) 21:319–330 DOI 10.1007/s00138-008-0164-9
ORIGINAL PAPER
License plate localization based on a probabilistic model Rami Al-Hmouz · Subhash Challa
Received: 12 August 2007 / Revised: 25 June 2008 / Accepted: 29 July 2008 / Published online: 30 September 2008 © Springer-Verlag 2008
Abstract Extraction of the license plate region is the challenging first step in the license plate recognition system. We propose a novel feature fusion concept for plate extraction. The image-feature extraction process is modeled as a feature-detection problem in noise. The geometric features are probabilistically modeled and detected under various detection thresholds. These detection results are then fused within the Bayesian framework to obtain the features for further processing. Along with a probabilistic model, a pixels voting algorithm is also tested through threshold variation. Keywords
LPR · Plate localization · Bayes’ rule
1 Introduction License plate recognition (LPR) is a technology for automatically identifying vehicles using developments in the image processing and character recognition fields. All LPR applications consider the license plate as the vehicle’s signature and fingerprint for identification purposes. Typical applications of LPR include unattended parking lots, surveillance and security control of restricted areas, traffic law enforcement and automatic toll collection. A typical LPR system structure is composed of four modules: R. Al-Hmouz (B) Faculty of Engineering, University of Technology, Sydney, Broadway, PO Box 123, Sydney, NSW 2007, Australia e-mail:
[email protected] S. Challa Faculty of Engineering, The University of Melbourne, Melbourne, Australia e-mail:
[email protected]
– Images acquisition module: acquires images from video sequences and feeds them to the system input – LP extraction module: locates and extracts the plate from the video sequences – Character segmentation module: separates characters from each other – Character recognition module: identifies characters from images. Traditional LPR systems have been developed over the last twenty years. The performance of these systems is heavily dependent on the environmental set-up, such as the quality of cameras, special lighting and other forms of controlling the environment. The improvements in LPR performance have been tackled from the hardware point of view, as well as through intelligent and heuristic solutions. Any LPR system must be able to cope with: – – – – –
Poor image resolution Blurry images Weather conditions and different illuminations Non-uniform lighting across the plate. Non-character symbols and the license plate being obscured – Several plate standards. Most license plate localization algorithms exploit thresholding techniques like Otsu, Hysteresis, adaptive and other thresholding techniques to extract the license plate from the image. However, a clear segmentation of plates under various illumination conditions is very difficult to achieve using one threshold. Hence we propose an approach based on the fusion of multiple thresholds to ensure that the object is segmented at least once in a given image. The clusters’ features are
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then modeled as a joint probability distribution over the feature space, and all data from different thresholds are used to update the posterior distribution using Bayes’ rule. In this paper, first, we present previous approaches in the literature and discuss their applicability to the plate extraction problem, emphasizing the plate features method, and the edge detection approach to extract license plate, followed by signature analysis techniques and artificial intelligence methods. Next, we present our approach of modeling the license plate features as random variables, and propose the use of the Bayes’ rule to effectively use multiple thresholds. Finally, we present the experimental results and end with conclusions.
2 Related studies License plate extraction methods can be categorized into four main groups. 1. Plate features: In [1], the color of the plate was used as a feature, the image was fed to a color filter, and the output was tested in terms of whether the candidate area had the plate’s shape or not. Wang et al. [2] used a special filter instead of a color filter. The filter was convolved with the gray image, as the output would be the character-shaped objects, and the candidate regions were scanned to verify the nominated region. In [3] and [4], a threshold-based method was used. A threshold was selected to segment the images into black and white regions, as shown in Fig. 1, The regions were tested with connected components analysis to verify the plate region. Comelli et al. [5] used a test based on the character size and the distance between two characters. They reported a 91.07% success rate. All the color-based algorithms suffer from the illumination condition, because colors can be seen differently under different illuminations, also, plates have various colors and sizes. A threshold-based method will accordingly not guarantee that the plate region will be among the candidate regions. 2. Edge detection: The algorithms use the edges of the plate and the characters’ edges as a reference point for extracting the plate. The pixels’ intensities of characters and plate edges are completely different from the intensities in the neighboring pixels, because they always come in different colors. The edges in an image can be detected by the gradient process (derivative), for example, a Sobel mask can be taken with an optimal Otsu threshold [6] to detect the vehicle’s edges, as shown in Fig. 2. The second step of edge detection algorithms is to locate the edges of the plates from the black and white image. The authors of [7] and [8] used mathematical morphologies on the edges of the image, which included dilating, closing and erosion with the addition of the connected component analysis to extract the plate. In [9,10], a Hough transform was
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Fig. 1 a Original image, b black and white image format
Fig. 2 Edge-detection method
applied on the edge image. Then the lines that cross the plate frame were determined and a rectangular-shaped object that matched the license plate was extracted. Anagnostopoulos et al. [11] developed a method to describe the irregularities in the plate region, using the statistics of mean and variance for two sliding concentric windows
License plate localization based on a probabilistic model
(SCW). The two windows scanned the whole image pixel by pixel, and the ratios of the mean and the variance of the two windows were determined to decide whether the concentric pixel was an irregular pixel. This method is similar to edgebased methods, because it depends on the intensity values of pixels in regard to the neighboring pixels. All edge algorithms encounter a problem when the borders of the license plate do not exhibit much variation from the surrounding pixels. Also, edge detection uses a threshold that needs to be determined in which it cannot be uniquely obtained under various conditions and illuminations. 3. Signature analysis: Dlagnekov [12] implemented the AdaBoost algorithm that includes the Haar-like features which Viola and Jones [13] used for face detection. Windows of several sizes scanned the image to locate the plate region. Any windows of black and white combinations was considered as a feature, and in each window, multiple features could be generated. Plate features were classified through weak classifiers; the classifiers were combined in the form of cascaded weak classifiers to structure a strong classifier. The classifier parameters were obtained through training the classifiers on plate and non-plate images. Matas and Zimmermann [14] proposed an algorithm to detect license plate and road sign. They selected the text region from the set of extremal regions (connected components) as a basic signature of license plate. The algorithms exploit the fact that the plate has characters and digits (text) that are clearly visible when various features are tested. However, some areas in the scene that have text might be erroneously labeled as a plate area. Elliman and Lancaster [15] analyzed the use of Fourier spectra to distinguish between different areas in an image that has spatial information. Broumandnia et al. [16] scanned the image vertically with N rows and counted the number of edges in each scan to locate the horizontal line on which the plate lies. Instead of using the number of edges, Barroso [17] used an analysis of the maxima and minima to locate the horizontal lines in which plate signature was defined as the characteristics of the horizontal line, such as distance and amplitude. Acosta [10] used Periodograms for each line and column in which the lines and the columns of higher energy levels were selected to represent the plate’s region. In [18], Haar scaling for wavelet transform was used to extract the plate. The plate was extracted through locating the high horizontal variation in the Low-High filter sub-image and determining the vertical edges in the High-Low filter sub-image. 4. Artificial intelligence: Nijhuis et al. [19] utilized the characteristics of Dutch plates that shared a yellow background to form a membership function for each pixel through the histogram of RGB values. The other fuzzy property was the pixel texture, which was taken from the pixel’s gray level and that of its neighbors. Zimic et al. [20] applied the same concept of fuzzy logic. They divided the image into
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plate-sized rectangles, then they applied membership functions on two levels. First, each pixel was considered as bright or dark according to its membership function. The second membership represented the sequence of brightness and the sequence of darkness. The authors of [21] successfully located the plate in 1,065 images, using fuzzy logic at a rate of 97.9%. However, the fuzzy logic method is sensitive to illumination, because it depends on the color scheme of the images. Park et al. [22] used two timed delay neural networks (TDNNs) as a horizontal filter and vertical filter to locate Korean license plates. The intersection between the two filters located the plate region. Information about the hue, saturation and intensity of one dimensional cross-sections of an image (window) comprised the inputs to the neural networks. Variable plate sizes were extracted and a 97.5% success rate was achieved. A Pulse Coupled Neural Network PCNN was used by Chacon and Zimmerman [23] to locate license plates; candidate regions were produced from the output of the neural network, and the candidate regions were analyzed by Fourier transform to locate the plate region. If the plate could not be located, the variables of PCNN were changed and the algorithm was repeated. An 85% success rate was reported. The neural network success rate mainly depends on the training process, which needs to cover as many cases as possible under various illumination conditions. Since the inputs of the neural network are pixels’ values in the gray levels, which have many variations, yet again the color problems appear in the artificial method, but in less damaging form. Combining multiple classifiers has been pursued for improving the accuracy of single classifiers [24]. A classifier fusion-based detection algorithm was introduced by [25] to extract the optimal features from the candidate plate region. Almost all methods depend on selecting a threshold, especially when converting the image into black and white format. As a result, the threshold value is not unique, because of the uncertainties and various lighting conditions. Therefore, various thresholds have to be tested in order for the plate to be correctly segmented at least once.
3 Plate extraction approaches Selection of the threshold is critical for the quality of binary images and in terms of feature extraction. Multiple binary images from a gray-scale image can be obtained using multiple thresholds. Here, we will introduce three methods of plate localization based on threshold variation. In the first method, a deterministic method based on geometric features is used to locate the plate; pixels vote for the plate area that appears most frequently in multiple thresholds. In the second method, geometric features are modeled as random variables. Bayes’ rule is used to update the posterior under the normal
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distribution assumption of all uncertainties. Finally, a method based on local thresholds with the combination of the probabilistic approach is used when the second method fails to extract the plate.
particular image. Moreover, plates come in various colors and shapes, and therefore varying the thresholds will help the plate to appear as a connected region at least once in the binary images.
3.1 Image processing
3.2 Plate features
The first step in the LPR system consists of acquiring frames from a digital camera. Light illumination and the quality of the selected frame have an effect on overall LPR systems and especially on the first part of plate extraction. However, some image-processing techniques can be used to estimate the illuminant [26] or enhance image quality [27] before the image is passed to the next module of plate extraction. Initially, the colored input image is converted into a grayscale image using the values of R,G and B of each pixel as shown in Eq. 1. A selected threshold T is chosen to convert the image into a binary format image (BW) (0,1). The pixel vi j in the gray level is mapped into 1 and 0, as in Eq. 2. The Otsu technique [6] can be used to find the optimal threshold. However, thresholding the image could result in an unconnected plate region or the plate region could be totally missed, as can be seen in Fig. 3.
The binary image consists of black and white regions; the white regions appear as connected areas or clusters among the black regions. The nominated plate will be among the white areas if the threshold has been chosen carefully. The clusters of BW images are examined to determine whether the connected-region features fall into the standard license plate features or not. The region feature can be identified based on connected component analysis. The most common features which can be extracted from the connected component are:
vi j = 0.299.Ri j + 0.587.G i j + 0.114.Bi j 1 if vi j ≥ T BWi j = 0 otherwise
The length or width of the connected region is the number of pixels in the cluster’s length and width, area (a) is the number of white pixels in the connected area, density (d) is the ratio of black pixels to white pixels and (g) is the number of edges in the center of the plate, as described in Fig. 4.
(1) (2)
The Otsu threshold fails to segment the plate under various illumination conditions. Hence, multiple thresholds including the Otsu threshold are used to convert the image into black and white images in order to guarantee that the plate region will appear at least once as a connected region in the view. Each image is considered as a sensor and it informs with the plate location if the plate can be extracted from that
Fig. 3 Unconnected plate region
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1. 2. 3. 4.
length/width ratio u of the connected region. Background area a. Character/plate area density d. Number of edges in the plate center g.
3.3 Deterministic approach: pixel voting A license plate can be correctly segmented, when a right threshold is chosen in which all the plate features fall into the standard plate features. However, plates come in different shapes and sizes, minimum and maximum values of features were determined in order to test the measured plate features whether they fall between these two values. The minimum and maximum features’ values were determined from the
Fig. 4 Plate features
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Fig. 5 Pixel voting
available plate images. Normally, one cluster from the black and white image passes the test, and this cluster is picked up as the plate region. Selecting a threshold which can segment the plate is not an easy task, therefore, multiple thresholds are used to generate multiple black and white images. For each black and white image, The winning cluster’s pixels will be given one vote under that particular threshold. After all thresholds have been tested, the pixels vote for the plate region, and the votes are counted from all black and white images. For example, if a plate region appeared four times as a standard plate region under four different thresholds, then the pixels that belong to the plate cluster will give four votes for that region, and the pixels in the non-plate area will have no votes. The pixels vote for the plate region instead of the clusters because the clusters are not exactly the same under different thresholds; the voting process is applying the intersection among the wining clusters that appear from different thresholds. In Fig. 5, the plate region appears three times as a candidate region, which implies that the plate has been extracted in three BW images. Further tests can be carried out on the plate region to confirm the result; for example, the number of characters can be counted after the plate is extracted, and test whether they are aligned or not.
ties are modeled as Gaussian distributions. The distributions of cluster features can be represented as: x − mx exp − p(x) = √ 2σx2 2π σx 1
(3)
where m x is the mean of plate feature, σx is the variances of plate feature. Figure 6 shows the plate features statistics which have been calculated from 1106 plate-image. The features’ statistics are the same regardless the position of the camera except for the area, therefore, different camera positions implies different distributions for the plate area. Assuming the independence of feature measurements, the distribution can be joined in a joint distribution, as depicted in Eq. 4. Each connected region in the black and white images will have a certain probability based on its features. Consequently, the cluster that has features close to the plate feature will have a higher probability than other clusters and it might be selected as the candidate plate region if the other thresholds support the same cluster. 1 1 T −1 exp − (z − s) V (z − s) p(z) = (2π )1/2 (detV )1/2 2 (4)
3.4 Probabilistic approach: global binarization Black and white images are obtained from the main images by taking multiple thresholds; each black and white image will produce clusters. The cluster features are modeled as random variables U, A, D and G for the length/ width ratio (u), area (a), plate density (d) and number of edges (g) consecutively. Each cluster will suffer from uncertainties; these uncertain-
where: ⎡ ⎤ u ⎢a ⎥ ⎥ z=⎢ ⎣d ⎦ g
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The scores are then normalized for each threshold as shown in Eq. 7.
Let R1 , R2 , . . . , Rn be the connected regions in a binary BW image and bw1 , bw2 , . . . , bwm be the BW images that have been produced from t1 , t2 , . . . , tm thresholds. The p(Rn (z)/bwm ) can be calculated as shown in Eq. 5. p(Rn (z)/bwm ) =
1 (2π )1/2 (detV )1/2 1 ×exp − (Rn (z) − s)T V −1 (Rn (z) − s) 2
(5)
Rn (z) is the measured features vector of a cluster Rn . The probability of the cluster is modeled as a joint distribution function. All connected regions from all thresholds will have probabilities based on their features. The clusters from different thresholds cannot be fused, because they
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have different sizes and their centers are slightly different. Therefore, the probability of the cluster is assigned to the pixels that belong to the same cluster. The pixels that do not have a cluster (black pixels) are given a small probability in order not to squash the pixels’ probabilities when they have values from other thresholds, the score for each pixel for any given BW image is calculated in Eq. 6. if vi j ∈ Rn p(Rn /bwm ) (6) S(vi j /bwm ) = Min( p(Rn /bwm ))/2 otherwise
S(vi j /bwm ) i, j S(vi j /bwm )
P(vi j /bwm ) =
(7)
The process of using various thresholds and calculating the pixels’ probabilities emulates multiple sensors reporting for a particular event. Each pixel in the image will have multiple probabilities from multiple thresholds; the values of these probabilities represent whether the pixel belongs to the plate region or not. Now, given a set of pixels’ probabilities P(vi j /bw1 ), P(vi j /bw2 ),…, P(vi j /bwm ), we would like to recover P(vi j / bw1 , bw2 ,ldots, bwm ). Starting with bw1 , bw2 , the corresponding probability P(vi j / bw1 , bw2 ) can be calculated using recursive Bayesian updating as shown in Eq. 8.
License plate localization based on a probabilistic model
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Fig. 7 Pixel probabilities
P(vi j /bw1 , bw2 ) =
P(bw2 /vi j , bw1 )· P(vi j /bw1 ) p(bw2 /bw1 )
(8)
Assuming conditional independence of the measurements, recursive updating is simplified to Eq. 9. P(vi j /bw1 , bw2 , . . . , bwm ) = ∆
m
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(9)
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∆ is the normalization all over the image pixels. Basically, The probability of the pixel vi j consists of multiplying its probabilities from various thresholds and then normalizing the results for all pixels. In order for the plate low pixels’ probabilities to be extracted with other plate areas, a window of a plate size is extracted at the region of the pixels’ highest probabilities. In general, threshold levels between 0.15 up to 0.6 (0-1 scale) with a step of 0.05 can be used to obtain black and white images that have at least one clearly segmented plate. Figure 7 shows the updated pixels’ probabilities, it is clearly seen that the plate’s pixels triumph over other pixels. In addition, for images that are badly affected by illumination, we should cover all threshold values from 0.1 to 0.9 to insure the plate is segmented at least in one of the thresholds. In the case of other regions appearing more than the plate region, with their pixels’ probabilities being higher than the plate region’s pixel probabilities, as a result, another region will be picked up as a candidate region. However, this case exists only if the non-plate region has the same features as standard plate features.
in each pixel scans the image, as can be seen in Fig. 8. A window size of (40 × 120) is sufficient for the plate to be appeared in it. For each window location, an Otsu threshold is used, and clusters’ probabilities are computed according to Eq. 5. The pixels’ probabilities are calculated as shown in Eq. 10. For clusters C1 , C2 . . . C N generated from a window location centered at vi j , the pixel vi j will have the highest probability of the generated clusters. At the end, a bunch of pixels in the plate region will inform with high probabilities, as shown in Fig. 9. The plate region will be populated with high probabilities because the generated cluster in the plate region are almost the same. The window that is located at the center pixel of these high probabilities is considered as the nominated license plate. P(vi j ) = Max(P(C1 ), P(C2 ) . . . P(C N ))
(10)
where vi j is the center pixel of the sliding window, P(Ci ) is the probability of cluster i in the sliding window which has N clusters.
3.5 Probabilistic approach: local binarization When it is impossible to find one single threshold that will segment the plate using a global threshold, it is preferred to use a locally adaptive threshold. In this case, a sliding window
Fig. 8 Local thresholds
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Fig. 10 Car samples
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License plate localization based on a probabilistic model
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Fig. 11 Thresholded images
The local threshold of the sliding window can also be used in the voting method, however, the plate region will have only one vote, and whenever there are two regions, more tests should be carried out to differentiate between the plate and the non-plate regions. 3.6 Combination among approaches The probabilistic approaches can be fused to update posterior distribution in Eq. 9. The local threshold probabilities can be considered as if it has been computed from bwm+1 image. The mis-located number plates are all of the connected regions of the plate area which do not take a plate shape, because of an indistinguishable frame around the plate. In addition, the area of a connected plate region might not be formed under any threshold value, because the plate’s background color is almost the same as the color of the vehicle, in which the plate regions could not be formed at all. Consi-
dering more thresholds could not be crucial for most cases, since there is no definite black frame around the plate. Therefore, another method to locate the license plate can be used, and the results from both methods can be fused to enhance the performance for these special cases. After extracting the plate from other plate localization approach(i.e edge, signature, etc), the plate is binarized and the pixels’ probabilities are calculated as described in Eqs. 5, 6 and 7, and again the fusion among techniques can be achieved in Eq. 9. 4 Results The algorithm has been tested on 1785 colored images. All images were obtained from Mobitx camera (http://www. mobotix-camera.com/) with 480 × 640 pixels which was located in the entrance of a car park. The images cover different colors and sizes of New South Wales (NSW), Australia plates under various illuminations (sunny, night,
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Fig. 12 Unsuccessful plate localization images
Table 1 Plate localization accuracy Method
Accuracy (%)
Deterministic (Voting)
61.36
Probabilistic with global threshold
90.65
Probabilistic with local threshold
78.26
Combining global and local threshold
93.78
cloudy, etc). The camera is triggered by motion detectors to capture car images, therefore, a license plate exists in all frames. Figure 10 shows samples of the tested images. The threshold variation method is used to localize the plate; 39 thresholds are used to convert an image into 39 black and white images. The values of thresholds vary from 0.1 to 0.87, with a jump of 0.02. Figure 11 shows the thresholded images of a car image. Table 1 shows the accuracy of plate localization using the threshold variation methods. The globally thresholding
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probabilistic method outperforms other approaches, because probabilities are given for pixels regardless of whether the plate’s cluster covers the whole plate or not. As the threshold increases or decreases, the plate cluster will shrink or widen and in both cases probabilities are given to the pixels which are high enough to dominate over other probabilities. When using the deterministic method, the plate cluster should be formed as an identical plate cluster whose features should approach the standard plate features. Figure 12 shows a sample of images in which both previous methods failed to localize the plate, while the locally probabilistic method was able to localize the plate. In these images the plate cluster cannot be formed under any global threshold. This resulted in the plate area being part of a very big cluster that has a very small probability, as can be seen in Fig. 13. The reason behind that is one of these: – The black frame around the frame is broken or damaged. – Car parts cover plate’s edges.
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Fig. 13 Unsuccessful plate localization thresholded images
– The car and plate are both white and there is no distinguishable frame around the plate.
The local threshold method still cannot compete with the global threshold method, because many areas that don’t belong to the plate still have high probabilities as described in Fig. 9. However, the local threshold is less sensitive to the broken frame. An accuracy of 93.78% was obtained when combining both global and local thresholds through Bayesain fusion. Comparison based on accuracy among plate localization algorithms is not an easy task, since some algorithms put some restriction on the algorithms such as the distance, angle and illumination conditions while they reported high rate of plate localization. Also, the absence of a standard data
set as well as some algorithms had been tested on very small portion of vehicle images [16] make the comparison unfair for some algorithms.
5 Conclusion Generally, segmenting objects from images is an open problem for many applications. Several algorithms are presented, but no algorithm can guarantee achieving 100% of object segmentation. License plate extraction is the first step in recognizing the plate. In other steps of segmentation and recognition, the algorithms depend on the result of plate extraction, which makes the extraction crucial for recogni-
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tion. Most approaches for plate extraction use a threshold to convert the image into black and white, and after that different approaches can be used to extract the plate. The Otsu threshold is the most common approach to convert the image into black and white. However, Otsu cannot guarantee that the plate will appear as a connected region, because of the uncertainties produced in the image. Multiple thresholds are proposed to convert the image into binary images; each binary image acts as a sensor that informs with new data, and results from binary images are fused using a probabilistic approach and a deterministic approach. In the probabilistic approach, the plate’s geometric features are modeled as random variables which are normally distributed; the probability of connected regions is calculated through the fusion of region features. In the deterministic approach, the candidate plate region is extracted for every threshold, the pixels which appear the most frequently will vote for the plate region. If either the probabilistic approach or the deterministic approach fails to extract the plate, a probablistic approach of local thresholding method can be utilized by using a sliding window that scans all areas in the image; the neighboring pixels that have high probabilities are considered as the plate region. Our algorithms can be implemented in any kind of object extraction problem that depends on geometric features to extract the object. For further work, the concept of threshold variation can be extend to be used in the character segmentation module.
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