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License Plate Localization and Character Segmentation With Feedback Self-Learning and Hybrid Binarization Techniques Jing-Ming Guo, Member, IEEE, and Yun-Fu Liu

Abstract—License plate localization (LPL) and character segmentation (CS) play key roles in the license plate (LP) recognition system. In this paper, we dedicate ourselves to these two issues. In LPL, histogram equalization is employed to solve the lowcontrast and dynamic-range problems; the texture properties, e.g., aspect ratio, and color similarity are used to locate the LP; and the Hough transform is adopted to correct the rotation problem. In CS, the hybrid binarization technique is proposed to effectively segment the characters in the dirt LP. The feedback self-learning procedure is also employed to adjust the parameters in the system. As documented in the experiments, good localization and segmentation results are achieved with the proposed algorithms. Index Terms—Character recognition (CR), character segmentation (CS), license plate recognition system (LPRS), plate localization.

I. I NTRODUCTION

T

HE LICENSE PLATE recognition system (LPRS) is a product of modern life, which can be separated into the following three parts: 1) detecting the location of the license plate (LP), namely, LP localization (LPL); 2) segmenting the characters inside the LP, namely, character segmentation (CS); and 3) recognizing the meaning of the characters, namely, character recognition (CR). Many state-of-the-art methods have been addressed in the progress of the LPRS. Some methods have also been marketed. However, most schemes are restricted by some practical restrictions, such as recognition time, lighting conditions, unstable environment, image resolution, etc. To develop a robust system that adapts these various issues is a challenge. Regarding the LPL, in general, the literature can be separated into the following two parts: 1) based on the textures of the LP and 2) based on the colors of the LP. The methods are discussed below. The methods based on textures mainly exploited the aspect ratio [2], [9], [12], the contrast variations [10], the uniform Manuscript received November 11, 2006; revised August 3, 2007; accepted August 22, 2007. This work was supported by the National Science Council, R.O.C., under Contract NSC 95-2221-E-011-218. The review of this paper was coordinated by Dr. M. A. Masrur. The authors are with the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan, R.O.C. (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TVT.2007.909284

distribution of the characters [4], and the ratio between background area and character area [6], [8]. Among these, the approaches in [2] and [12] are applied to the LPRS in Saudi Arabia. The main idea is to process the image as a grayscale image and employ the Sobel edge detection, projection, and seed-filling algorithm [7] to remove the regions unrelated to the LP. The result is then filtered by the aspect ratio and object connections. However, the method is not capable of dealing with the complex environment and rotation. Nonetheless, the rotation problem can be eased by considering the rotation extent [9]. Hsieh et al. [4] employed the Wavelet transform to decompose the image into four bands (i.e., HH, HL, LH, and LL). The searching range of the LP is restricted according to the image property in the LH band to improve the processing speed. The projection method, edge density, and aspect ratio are then exploited to locate the LP. This approach is proven to be effective in withstanding the variation of the lighting, contrast, and rotation and is also capable of locating multiple plates. Wu et al. [8] adopted the morphological projection to localize the LP. The method exploited the ratio between background area and character area and used the opening operation to blur the image. The processed image is compared to the original image. The discrepancy part is then processed with the projection method to locate the LP. The advantage of this method is high efficiency, whereas the disadvantage is easily undergone by the interference of the lighting effect. The methods based on the colors are discussed as follows. Jia et al. [6] adopted the mean shift to blur the image and then localized the LP by applying the Mahalanobis distance linear classifier to classify the candidate regions according to rectangularity, aspect ratio, and edge density. Syed and Sarfraz [1] adopted the vector angle measure [3] to retrieve the color edge and then enhanced the edge. The object connection is applied to the classified regions separated by the edges and further locates the LP. Chang et al. [5] converted the red–green–blue (RGB) color space into the hue-saturation-intensity (HSI) model and then performed the blurring process to reduce the interference of noise. The clustered edge property of the LP is exploited to cooperate with the HSI color spaces to locate the LP. The advantage of this method is the capability of withstanding the interference of the environment. Yang et al. [11] simultaneously adopted textures and colors to locate the LP, where the color collocation of the plate’s background, the characters, and the plate’s structure are

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Fig. 1. Character configurations of the LP in Taiwan.

exploited. The plate’s region is then binarized, segmented, and recognized. The advantage is the high efficiency and good localization result. The disadvantage is the vulnerability in dealing with low contrast or poor color. In this paper, an LPL method and a CS method are presented to overcome the issues raised in the literature, which include complex environment, rotation, lighting, and low contrast. Briefly, histogram equalization [13] is employed to solve the lighting and contrast problem. The texture property, aspect ratio, and color similarity are used to address the complex environment issue. The Hough transform is adopted to correct the rotation problem. Moreover, the feedback self-learning strategy is also employed to adaptively adjust the parameters. Finally, the proposed hybrid binarization is applied to solve the dirt problem in CS. The rest of this paper is organized as follows. Section II introduces the LPL. The CS is presented in Section III. Section IV describes a series of experiments that are performed to test the performance of the algorithm. The conclusions are drawn in Section V.

II. LPL The main difficulty of the LPL is the environment. For example, outdoor parking spaces have at least one more problem than indoor parking spaces because of the influence of sunlight. Hence, the performance of most recognition systems varies a lot according to the energy of the sunlight. In this paper, histogram equalization is adopted to reduce the sunlight or low-contrast problems. The other solution is the well-known contrast stretching, which is also capable of improving the lowdynamic-range image. However, it performs poorly in backlighted conditions, which can be totally solved with histogram equalization. The typical LP in Taiwan has two English letters and four Arabic numerals. The configurations of the different cars are somewhat different; some cars have English letters at the front, whereas some are at the back, as shown in Fig. 1. Nonetheless, the colors are united as black and white. Hence, the number of the characters and the color are employed in this paper to identify LP. In some cases, the radiators or bumpers have similar textures as the plates. In this case, the high gradient property between black characters and white background, as well as the aspect ratio, are employed to remove the irrelative objects. Sometimes, the relative positions between camera and car cause rotation in LP. In this regard, the Hough transform is employed to reduce the rotation effect. The overall architecture of the LPL is depicted in Fig. 2, and the details are presented as follows.

Fig. 2.

Architecture of plate localization.

A. Preprocessing Preprocessing is mainly used to enhance the processing speed, improve the contrast of the image, and reduce the noise caused by equipment or environment. It is composed of the following three steps. 1) Searching Range Reduction: In general, the interested object is placed at the center of a picture when a shot is made. Hence, in this paper, the region of interest is fixed at the center 4/9 area, as illustrated in Fig. 3. 2) Histogram Equalization Enhancement: Many studies adopted conventional contrast stretching to enhance the lowcontrast images as f (gi ) =

gi − gmin × 255 gmax − gmin

(1)

where gi is the input pixel value, and gmax and gmin correspond to the maximum and minimum pixel values, respectively.

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The E-map is obtained by applying Sobel edge detection on the I-map, as described below. Sobel edge detection is performed with a 3 × 3 mask, which covers a region denoted as   A B C  D f (x, y) E  (5) F G H

Fig. 3.

where f (x, y) denotes the center pixel value, and the variables A–H represent the peripheral pixel values in this mask. The corresponding reconstructed value is defined as  (6) E(x, y) = H_gradient2 + V _gradient2

Search range reduction.

However, this method performs poorly when an image has a low dynamic range. Conversely, the histogram equalization achieves a good result for a low-contrast and dynamic-range image. An example of performance comparison is given in Fig. 4, where it is clear that the one processed with histogram equalization has a better contrast than contrast stretching. Notably, the histogram equalization does not need to be applied all the time, as indicated in Fig. 2. It is only enabled when no candidate object matches the plate’s property, e.g., texture or aspect ratio. The processed speed can be improved with this feedback self-learning strategy. 3) Median Filtering: Some images may undergo noise interference. To solve the problem, many filters are possible candidates, e.g., uniform filter, Gaussian filter, median filter, etc. In this paper, the median filter is adopted to remove the noise, since it preserves most of the fidelity of an image. The method is described by g(x, y) = median {f (x − k, y − l), (k, l) ∈ P DR}

(2)

where f (·) and g(·) denote the original image and the corresponding processed result, respectively. The variable predefined region (P DR) represents the covered region by a median filter, e.g., 3 × 3 or 5 × 5. In this paper, the filter of size 3 × 3 is employed. The reason behind this is given in Section II-B2, where an experiment is given. Nonetheless, median filtering still blurs the image a bit. Hence, this preprocessing is exploited only when no candidate object matches the plate’s property, as discussed in Section II-A2. B. Localization In this subsection, the color information and textures of LP are employed to further locate the exact position. 1) Three-Map Retrieving: The LPs in Taiwan are all in the same color distribution, i.e., black characters and white background. In this paper, the Saturation map (S-map), Intensity map (I-map), and Edge map (E-map) are employed for CS. Among these, the S- and I-maps are obtained by converting the RGB color model to the HSI model as S =1 − I=

3 [min(R, G, B)] (R + G + B)

(R + G + B) . 3

(3) (4)

where H_gradient = C + 2E + H −A− 2D−F denotes the gradient along the horizontal orientation, and V _gradient = A + 2B + C − F − 2G − H denotes the gradient along the vertical orientation. The Sobel operation is known to be insensitive to noises. 2) Non-LP Region Removing: In general, LP has the strongest gradient in the E-map. The high-gradient-averaging (HGA) method is applied to remove the non-LP regions by 255 

f (Ei ) × Ei Eth = i=0255  f (Ei )  i=0 Ei , Ei > Eth Ei = 0, Ei ≤ Eth .

(7)

(8)

The first HGA removes regions with lower gradients than the average gradient of the E-map. The second HGA further removes regions with lower gradients than the average gradient of regions with higher gradients than the average gradient of the original E-map, and so forth. Fig. 5 shows the located rates with a different number of HGA and median filter size (the other parameters are fixed). It is clear that the one with the second HGA and median filter of size 3 × 3 achieves the best result. The remaining areas in the E-map are further filtered according to the texture of the LP, as described below. Each character in LP has 35 possibilities (0–9, A–Z, where the letter O and the number 0 are considered the same). If the LP is scanned with a horizontal line, the number of black to white (or white to black) is at least six and at most 14, as shown in Fig. 6. Based on this observation, a horizontal line in the E-map is reserved with six–14 black-to-white switched numbers. In the same way, a vertical line in the E-map is reserved with one to three black-to-white switching numbers. The remaining areas in the E-map are then connected with the following method. Suppose that the distances between each character of LP are lower than a predefined parameter CD, where the parameter CD is defined as CD = (width of the range reduced image)/20.

(9)

Two white pixels in the same horizontal line of the E-map with distance lower than CD are connected. Hence, many objects can be obtained with this method. These regions are then filtered according to the aspect ratio and area of the LP, as described below.

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Fig. 4. Performance comparisons between contrast stretching and histogram equalization. (a) Original image. (b) Histogram of (a). (c) Contrast stretching result of (a). (d) Histogram of (c). (e) Histogram equalization result of (a). (f) Histogram of (e).

with an area higher than 1100 pixels. These reserved objects are named candidate objects. Each horizontal line of a candidate object is checked if the black-to-white switched number is between six and 14. The object with the highest percentage (more than 80% horizontal lines) with this feature is determined as the LP. Otherwise, the original image is processed with a median filter or histogram, and the method described in this subsection is applied again. This method, called feedback self-learning, is an important feature in the proposed algorithm. At this stage, the LP is roughly detected via the textures of characters. III. CS Fig. 5. Located rate versus HGA number and median filter size.

Fig. 6. Example of characters crossed by a horizontal line. (a) Six black-towhite switched numbers. (b) Fourteen black-to-white switched numbers.

The aspect ratio of the LP is generally around 2.8–4.8. On the other hand, according to our experiments, the extracted LP of size lower than 22 × 50 = 1100 pixels is difficult to recognize. Hence, we reserved the objects that meet the aspect ratio and

In this procedure, the LP is assumed to be located by the methods described above. The CS is followed step by step, as introduced below, to separate each character in the LP for final recognition. The procedure of CS can be divided into the following steps: 1) correcting the rotation; 2) locating the positions of the characters; and 3) separating the characters and background with the proposed hybrid binarization. The details are given as follows. A. Rotation Correction The performance of CS is mainly influenced by two factors, i.e., rotation and dirt. The rotation problem can be solved

GUO AND LIU: LPL AND CS WITH FEEDBACK SELF-LEARNING AND HYBRID BINARIZATION TECHNIQUES

Fig. 7.

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Rotation correction with Hough transform. (a) Image matched SI map. (b) Skeleton of Sobel edge. (c) x–y to a–b. (d) a–b to x–y. (e) Corrected result.

with the Hough transform, as introduced below, and the dirt problem is solved with hybrid binarization, as introduced in Section III-B. The S- and I-maps described above are employed to determine the full region of the LP. First, the located region is expanded by (height of LP)/2 in vertical orientation and by (width of LP)/10 in horizontal orientation to ascertain including the edge of LP. Since only two colors (black and white) are in the LP, the corresponding S-map is with the lowest value compared to other regions. Conversely, the LP in the I-map is dominated by the white color due to the fact that the background generally has a larger area than the characters. The background part can be retrieved from the overlap part around the peak value (±25) of the histogram of the S- and I-maps. The Sobel operation is then applied again to the background to address the contour of the LP. The pixels in the skeleton of the contour are processed with the Hough transform. Herein, the skeleton is in the x–y spatial domain, which has information about features and positions. The Hough transform result, which is associated with the a–b domain, is a good observed position for determining the slope of an object. The transformation is conducted with y = ax + b

(10)

and hence b = −xa + y

(11)

where the variable a denotes the slope, and b denotes the interception. Each point in the x–y domain represents a straight line in the a–b domain. Since the upper and lower boundaries of the LP are the two longest lines in the x–y domain, the two most overlapped points in the a–b domain represent the two longest LP boundaries. The corresponding angles of the two most overlapped points may be slightly discrepant to each other. Hence, a more accurate rotation angle of the LP can be determined by the average of the two most overlapped points by the lines in the a–b domain. An example is illustrated in Fig. 7, where the center part of Fig. 7(a) is the region retrieved by the S- and I-maps. Fig. 7(b) shows the skeleton of the background part. The two red circles in Fig. 7(c) indicate the two most overlapped points in the a–b domain. The two red lines in Fig. 7(d) are obtained by the two points in the a–b domain, and the average slope of the two red lines is used to correct the rotation problem. The corrected result is shown in Fig. 7(e). The corrected LP is then normalized to 60 × 150 for further binarization processing.

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Fig. 9.

Fig. 8. Architecture of the hybrid binarization method.

B. Hybrid Binarization Technique Dirt generally causes failure in binarization, object connection, or in determining the size of an object. Sometimes, dirt has similar properties as the characters in LP. The global binarization (threshold with the average value) performs poorly in these cases, as shown in Fig. 10(f). For this, the hybrid binarization method is proposed as introduced below, and the architecture of the hybrid binarization method is depicted in Fig. 8. The average value of the LP is calculated and defined as 255 

GM 1 =

Smooth processed gray-level histogram.

Fig. 10. Hybrid binarization method. (a) Normal LP. (b) Dirt similar to white background. (c) Serious dirt. (d) Located plates. (e) Histogram after low-pass filtering. : Old threshold, : New threshold. (f) Binarized results with the old threshold. (g) Binarized results with the new threshold. (h) Binarized results with 10 × 10 block. (i) Binarized results with hybrid binarization method.

f (gi) × gi

i=0

IW × IL

.

(12)

The histogram peak on the left-hand side of GM 1 is denoted GL max , and the peak on the right-hand side of GM 1 is denoted GH max . The first valley on the right-hand side of GL max is denoted GL min , and the first valley on the left-hand side of GH max is denoted GH min . The average value of GL min and GH min is noted as GM 2 , which is the new threshold. An example is demonstrated in Fig. 9. The binarized result processed with GM 2 is shown in Fig. 10(g). The reason behind using GM 2 as the new threshold is to solve the problems of dark backgrounds or light characters. However, the dirt may appear in some local regions. Hence, we further refine the method

Fig. 11.

Block size optimization.

described above to address the local dirt problem, as introduced below. The obtained result processed with the method described above is divided into nonoverlapped blocks of size 10 × 10, which is proven to be with the best segmented rate, as shown in Fig. 11. The average of a block is denoted GM 3 . The average of the pixels with values lower than GM 3 is denoted Lavg , and the

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TABLE I LOCATED AND SEGMENTED RATES

TABLE II COMPARISONS OF VARIOUS METHODS

Fig. 12. Percentages of photo taking time.

average of the pixels with values higher than GM 3 is denoted Havg . The new threshold is defined as GM 4 = (Havg + Lavg )/2.

(13)

Then, we check if GM 3 = GM 4 . If yes, then GM 4 is the new threshold for binarizing the block; otherwise, the above procedure described in this paragraph is repeated. The binarized result is shown in Fig. 10(h). The local dirt is removed as desired. However, the block-wise method is too sensitive, which leads to a scattered result. Hence, the following hybrid binarization method is proposed to solve the problem. The LP has been normalized to 60 × 150 after the rotation correction procedure. The LP processed with the proposed method described above is further scanned from top to bottom with a horizontal line of length 22 pixels, where the number 22 represents (width of LP)/7. If no white pixel is within the horizontal line, the corresponding pixels in the LP processed with the global threshold, as shown in Fig. 10(f), are reset to black (background). By doing this, each character can be separated from the top and bottom noises. The same procedure is applied to segment each character. The LP is scanned from left to right with a vertical line of length 20 pixels, where the number 20 represents (height of LP)/3. If no white pixel is within the vertical line, the corresponding pixels in the LP processed with the global threshold are reset to black (background). The segmented results are shown in Fig. 10(i). However, some cases, as shown in Fig. 10(c), with serious dirt cannot be segmented with the proposed method. Nonetheless, the chance of these cases occurring is relatively low compared to the normal cases, since they will be exhorted by polices. IV. E XPERIMENTAL R ESULTS In this paper, the central processing unit with Intel Pentium 4, 3.2 GHz, and 512-MB memory is employed for the performance test. The test images include 332 different images of size 867 × 623. The distances between camera and cars are between 2 and 5 m. The indoor and outdoor environments are both included, and the images were captured in the morning, afternoon, and night, as shown in Fig. 12. In general, the located rate and the segmented rate perform the best in the morning, whereas it is worst at night, as shown in Table I. The overall located and segmented rates are 97.1% and 96.4%, respectively. The performance comparisons with various methods are shown in Table II. The proposed method is only inferior to the

method in [9]. Nonetheless, the method in [9] fails when the edge of the LP is blurry or unclear. Moreover, it also fails when two vertical edges beyond the LP occur at the same time. Finally, the complexity of the proposed system is organized in Table III in terms of the addition (subtraction) and multiplication (division) operations. Some variables are defined as follows: IW height of the original image; IL width of the original image; SW reduced height (2 × IW/3), as indicated in Fig. 3; SL reduced width (2 × IL/3), as indicated in Fig. 3; AW height in the a–b domain; AL width in the a–b domain; PW normalized height of the LP; PL normalized width of the LP; CW height of a character; CL width of a character; Num. Obj. object number after object connection. We take the HSI model conversion procedure as an example. According to (3), two additions, one subtraction, one multiplication, and one division are involved to achieve one pixel in the S-map. In (4), two additions and one division are involved to achieve one pixel in the I-map. Since the image is reduced in its size after the search range reducing procedure, the overall operations include SW × SL × 5 additions (subtractions) and SW × SL × 3 multiplications (divisions) to obtain the S- and I-maps. The operation numbers of the other procedures can be derived likewise. Moreover, the average execution times of LPL and CS using the test images described above are listed in Table III as well.

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TABLE III COMPLEXITY ANALYSES OF LPL AND CS

Since the feedback self-learning scheme is adopted in this system, the complexity is relatively high. Hence, the complexity reduction and the CR are left for future study. R EFERENCES [1] Y. A. Syed and M. Sarfraz, “Color edge enhancement based fuzzy segmentation of license plates,” in Proc. IEEE 9th Int. Conf. IV, 2005, pp. 227–232. [2] M. Sarfraz, M. J. Ahmed, and S. A. Ghazi, “Saudi Arabian license plate recognition system,” in Proc. IEEE Int. Conf. GMAG, 2003, pp. 36–41. [3] S. Wesolkowski and E. Jernigan, “Color edge detection using jointly Euclidean and vector angle,” in Proc. Vis. Interface, May 1999, pp. 19–21. [4] C. T. Hsieh, Y. S. Juan, and K. M. Hung, “Multiple license plate detection for complex background,” in Proc. IEEE 19th Int. Conf. AINA, 2005, pp. 389–392. [5] S. L. Chang, L. S. Chen, Y. C. Chung, and S. W. Chen, “Automatic license plate recognition,” IEEE Trans. Intell. Transp. Syst., vol. 5, no. 1, pp. 42– 53, Mar. 2004. [6] W. Jia, H. Zhang, and H. Xiangjian, “Mean shift for accurate number plate detection,” in Proc. IEEE 3rd ICITA, 2005, pp. 732–737. [7] A. R. Smith, “Tint fill,” Comput. Graph., vol. 13, no. 2, pp. 276–283, Aug. 1979. [8] C. Wu, L. C. On, C. H. Weng, T. S. Kuan, and K. Ng, “A Macao license plate recognition system,” in Proc. IEEE 4th Int. Conf. Mach. Learn. Cybern., Guangzhou, China, 2005, pp. 4506–4510. [9] M. Yu and Y. D. Kim, “An approach to Korean license plate recognition based on vertical edge matching,” in Proc. IEEE Int. Conf. Syst., Man, Cybern., 2000, vol. 4, pp. 2975–2980. [10] T. H. Wang, F. C. Ni, K. T. Li, and Y. P. Chen, “Robust license plate recognition based on dynamic projection warping,” in Proc. IEEE Int. Conf. Netw., Sensing Control, 2004, pp. 284–288. [11] Y. Q. Yang, J. Bai, R. L. Tian, and N. Liu, “A vehicle license plate recognition system based on fixed color collocation” in Proc. IEEE 4th Int. Conf. Mach. Learn. Cybern., Guangzhou, China, 2005, pp. 5394–5397. [12] M. J. Ahmed, M. Sarfaz, A. Zidouri, and K. G. AI-Khatib, “License plate recognition system,” in Proc. IEEE ICECS, 2003, pp. 898–901. [13] W. K. Pratt, Digital Image Processing. Hoboken, NJ: Wiley, 1991.

V. C ONCLUSION Proliferative applications using image processing or pattern recognition in LPRS, e.g., automatic charging system in parking spaces, vehicles management, and monitoring in traffic, have driven this research topic to its incresing popularity. LPL and CS are the two main issues addressed in this paper, since they play key roles in LPRS. In this paper, the histogram equalization is employed to solve the low-contrast and dynamic-range problems. The textures, aspect ratio, and color information are adopted to locate the LP. Moreover, the feedback self-learning strategy is applied to enhance the localized rate. In CS, the Hough transform is applied to correct the rotation problem. The proposed hybrid binarization method is then employed to reduce the annoying problem caused by dirt. As documented in the experiments, the proposed methods achieve good located and segmented rates.

Jing-Ming Guo (M’06) was born in Kaohsiung, Taiwan, R.O.C., on November 19, 1972. He received the B.S.E.E. and M.S.E.E. degrees from the National Central University, Taoyuan, Taiwan, in 1995 and 1997, respectively, and the Ph.D. degree from the Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan, in 2004. From 1998 to 1999, he was an Information Technique Officer with the Chinese Army. From 2003 to 2004, he was granted the National Science Council scholarship for advanced research from the Department of Electrical and Computer Engineering, University of California, Santa Barbara. He is currently an Assistant Professor with the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei. His research interests include multimedia signal processing, multimedia security, digital half toning, and digital watermarking. Dr. Guo is a member of the Institute of Electrical, Information, and Communication Engineers (IEICE) and a member of the Technical Committee of the IEEE Communications Society. He received the Acer Dragon Thesis Award in 2005, the Outstanding Paper Awards from IPPR and Computer Vision and Graphic Image Processing in 2005 and 2006, and the Outstanding Faculty Award in 2002 and 2003.

Yun-Fu Liu was born in Hualien, Taiwan, R.O.C., on October 30, 1984. He received the B.E. degree from Jinwen University of Science and Technology, Taipei, Taiwan, in 2007. He is currently a Master Student with the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei. His research interests include intelligent transportation systems, digital half toning, and digital watermarking.

License Plate Localization and Character ...

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