9、橫切。在這個階段，我們使用兩個水平削減和再算上該由這些橫切削減對象的數(shù)量。一候選人將被視為一個盤子，如果數(shù)量的減少選擇對象為每個板塊在一定范圍內(nèi)適當
通過實驗類型。這個數(shù)字必須在數(shù)量大致范圍在一類病毒顆粒的字符，我們有兩個合適的約束兩個越南板類型：預(yù)處理OCR的分割拍攝的圖像心室晚電位檢測從相機許可證帕泰字符:4≤1≤8排板候選人7≤2≤n時排板候選人16與N是禁對象的數(shù)量。候選人是滿足了上述兩個約束選定為最終結(jié)果。在我們的制度，我們實施的1 /3兩hoziontal削減和2 / 3的板候選人的高度。平均的數(shù)目切對象將被計算。這種評估有助于確定正確的板候選人。
(3).分割
要正確認識字
10、，我們要一車牌圖像二值圖像的設(shè)置只包含一車牌字符。這些形象將被傳遞到對于OCR的識別模塊。這個任務(wù)常見的算法是運用預(yù)測。然而，在一些情況下，無法正常工作。我們現(xiàn)在將描述我們在分割方法添加一些增強此方法。
我們用一個水平投影檢測和部分行排在二板。因為二進制圖像進行了調(diào)整板他們的傾斜角度為零，分割結(jié)果的行幾乎是完美的。與最低值的位置水平投影是啟動或在最后一排板。不同形式的行分割，字符分割更為困難，因為許多原因，如卡字符，螺絲，和泥覆蓋板。這些噪音事情的原因使用的字符分割算法垂直投影，有一些錯誤。在一些最嚴重的圖像質(zhì)量差板的情況下，一個字符可以分割成兩部分。我們應(yīng)用的若干制約因素比到一個字符的寬度
11、高度。我們尋求的最低值在垂直 投影，只有這給削減最低位置 件滿足所有預(yù)定義的限制被認為是
字符分割點。通過此增強，我們在這項任務(wù)中取得了較好的效果。經(jīng)過這一步,我們有一個人物候選人名單。并不是所有的考生實際上是人物形象。
到那時，我們可以重新評估候選人是否板是一盤或不檢查的字符數(shù)候選人。在越南，一盤只包含7或8字符。最后一盤的候選人，連同與他們的字符列表傳遞到OCR模塊負責確認。
(4).用于光學(xué)字符識別隱馬爾可夫模型
在這個系統(tǒng)中，我們使用的字符的HMM模型承認。我們的特點，在此模型中使用的在窗口中的比例前景像素。我們使用的9 9大小的窗口，這個掃描在圖像窗口中，從左至右，從上到下
12、
這些窗口可以由兩個互相重疊三分之二的大小。通過這種方式，我們有一個特征向量其中包括196值。
在識別模塊，我們需要一個字符分類成一個形象的36個班（26個英文字母：甲，乙，丙...
和10個數(shù)字字符：0，1，2 ...）。要培養(yǎng)我們的模型，我們使用的訓(xùn)練，是從圖像中提取套
病毒樣顆粒。對每類樣本數(shù)約為60.These提取樣品圖像實時心室晚電位一點點的噪音，所以在良好的訓(xùn)練，該模型可以正是認識到板的同類型的噪音。在最后一步，我們使用越南的一些具體規(guī)則病毒樣顆粒以提高準確性。我們了解到，第三次在車牌字符必須是字母，四是有時信，但通常是一個數(shù)字，其他位置當然是數(shù)字。
四.實證評價
我們
13、的系統(tǒng)進行了評價與越南兩個套車輛的車牌。圖像由索尼DC350數(shù)碼相機，具有800x600像素大小，在不同地點和時間。我們使用Microsoft Visual C++ 6.0，運行惠普工作站X2000奔騰IV，1.4千兆赫，512 MB的的RAM，Windows XP操作系統(tǒng)。
五.結(jié)論和未來工作
該系統(tǒng)運行良好的越南各類 心室晚電位的圖像，甚至抓傷，縮放板的圖像。在 此外，它可以處理多個板塊中的案件 相同的圖像，或不同類型的車輛，如摩托車 板，汽車板或車板。然而，它仍然有幾個 錯誤在處理劣質(zhì)板材。
我們正在數(shù)的算法在預(yù)處理模塊。其目的是探測地區(qū)的第一盤地區(qū)可能，從而減少 計算成本的心室晚
14、電位檢測算法。在 此外，我們打算結(jié)合的紋理為基礎(chǔ)的數(shù) 方法，和機器學(xué)習的方法來評價platecadidates。我們相信，這些將提高信息的準確性和該算法的速度進一步。
索引詞:車載車牌識別，實時系統(tǒng)，Hough變換，輪廓算法。
附件2：外文原文（復(fù)印件）
Building an Automatic Vehicle License-Plate
Recognition System
Abstract—Due to a huge number of vehicles, modern cities need to establish effecti
15、vely automatic systems for traffic management and scheduling.One of the most useful systems is the Vehicle License-Plate (VLP) Recognition System which captures images of vehicles and read these plates’ registration numbers automatically.In this paper, we present an automatic VLP Recognition System,
16、 ISeeCarRecognizer, to read Vietnamese VLPs’ registration numbers at traffic tolls.Our system consists of three main modules: VLP detection, plate number segmentation, and plate number recognition.In VLP detection module, we propose an efficient boundary line-based method combining the Hough transfo
17、rm and Contour algorithm.This method optimizes speed and accuracy in processing images taken from various positions. Then, we use horizontal and vertical projection to separate plate numbers in VLP segmentation module.Finally, each plate number will be recognized by OCR module implemented by Hidden
18、Markov Model.The system was evaluated in two empirical image sets and has proved its effectiveness (see section IV) which is
applicable in real traffic toll systems. The system can also be applied to some other types of VLPs with minor changes.
I. INTRODUCTION
The problem of VLP recognition is
19、a very interesting butdifficult one.It is very useful for many trafficmanagement systems.VLP recognition requires some complex tasks, such as VLP detection, segmentation and recognition.These tasks become more sophisticated when dealing with plate images taken in various inclined angles orplate imag
20、es with noise.Because this problem is usually used in real-time systems, it requires not only accuracy but also fast processing.Most VLP recognition applications reduce the complexity by establishing some constrains on the position and distance from the camera to vehicles, and the inclined angles.By
21、 that way, the recognition rate of VLP recognition systems has been improved significantly.In addition, we can gain more accuracy by using some specific features of local VLPs, such as the number of characters, the
number of rows in a plate, or colors of plate background, or the ratio of width to h
22、eight of a plate .
II. RELATED WORK
The problem of automatic VLP recognition has been
studied since 1990s.The first approach was based on characteristics of boundary lines.The input image was first processed to enrich boundary lines’ information by some algorithms such as the gradient filter, a
23、nd resulted in an edging image.This image was binarized and then processed by certain algorithms, such as Hough transform, to detect lines.Eventually, couples of 2-parallel lines were considered as a plate-candidate [4][5].Another approach was morphology-based [2].This approach focuses on some prope
24、rties of plate images such as their brightness,symmetry, angles, etc.Due to these properties, this method
can detect the similar properties in a certain image and locate the position of license plate regions.The third approach was texture-based [3].In this approach, a VLP was considered as an objec
25、t with different textures and frames.The texture window frames of different sizes were
used to detect plate-candidates.Each candidate was passed to a classifier to confirm whether it is a plate or not.This approach was commonly used in finding text in images tasks.In addition, there have been a num
26、ber of other methods relating to this problem focusing on detecting VLP in video data.
III. THE PROPOSED SYSTEM
Our system, ISeeCarRecognizer, consists of four
modules: Pre-processing, VLP detection, character
segmentation, and optical character recognition (OCR), in which the last three modul
27、es deal with three main problems of a VLP recognition domain.The VLP detection module receives images which have been processed by the preprocessing module – the first input module of this system.The resulted images of this module are sent to the segmentation module.The segmentation
module segments
28、 plate-images into separate characterimages.These character-images are then recognized by the OCR module and the final results are ASCII characters andnumbers in plates.
A. Preprocessing
Images taken from camera were processed by the preprocessing module.The purpose of this module was to enrich
29、the edge features.Because our detection method bases on the boundary features, it will improve the successful rate of the VLP detection module.The algorithms sequentially used in this module are graying, normalizing and histogram equalization.After having obtained a greyscale image, we use Sobel fil
30、ters to extract the edging image, and then thresholds the image to a binary one.We used the local adaptive thresholding algorithm for the binarization step.Especially, we develop an algorithm based
on dynamic programming to optimize its speed and make it suitable to real-time applications [1].The r
31、esulted images are used as inputs for the VLP detection module.
B. VLP Detection Algorithm
In boundary-based approach, the most important step is to detect boundary lines.One of most efficient algorithms is Hough transform applying to the binary image to extract lines from object-images.Then we
32、look for two parallel lines, whose the contained region is considered platecandidates.However, the drawback of this approach is that the execution time of the Hough transform requires too much computation when being applied to a binary image with great number of pixels.Especially, the larger image t
33、he
slower the algorithm is. The speed of the algorithm may beimproved by thinning image before applying the Houghtransform.Nevertheless, the thinning algorithm is also slow. This limitation makes the approach unsuitable for real time
traffic management systems.The algorithm we used in this system
34、is the combination of the Hough Transform and Contour algorithm which produces higher accuracy and faster speed so that it can be applied to real time systems.
1) Combine Hough Transform and Contour Algorithm for Detecting VLP
Our approach is as follows: from the extracted edging image, we use t
35、he contour algorithm to detect closed boundaries of objects.considered as a plate-candidate.Since there are quite few
(black) pixels in the contour lines, the transformation of
these points to Hough coordinate required much lesscomputation. Hence, the speed of the algorithm is improved significant
36、ly without the loss of accuracy .
However, some plates may be covered by glasses ordecorated with headlights.These objects may also have the shape of two interacted 2-parallel lines, and therefore, arealso falsely detected as plate-candidates. To reject suchincorrect candidates, we implement a modu
37、le for evaluating whether a candidate is a plate or not.
2) Plate-Candidates Verification
From the two horizontal lines of a candidate, we can calculate exactly how inclined it was from horizontal coordinate. Then we apply a rotate transformation to adjust it to straight angle. After processed,
38、these straight binary
plate-candidate regions were passed to a number of
heuristics and algorithms for evaluating.Our evaluating plate-candidates algorithm bases on two
main steps, which are taken respectively. The two steps are:(a) evaluate the ratios between the heights and the widths of the ca
39、ndidates, (b) use horizontal crosscuts to count the number of cut-objects in the candidates.In this stage, we check and only select out candidates that have the ratios of width to height satisfying pre-defined constraint: minWHRatio < W/H < maxWHRatioSince there are two main types of Vietnamese plat
40、es: 1-row and 2-row , we have two adequate constraints for two types.3.5 < W/H < 4.5 with 1-row plate-candidates0.8 < W/H < 1.4 with 2-row plate-candidatesThose candidates which satisfied one of the two aboveconstraints are selected and passed to the nextevaluation.Evaluate by using horizontal cross
41、cutsIn this stage, we use two horizontal cuts and then count the number of objects that are cut by these crosscuts.
A candidate will be considered as a plate if the number of cut objects is in the given range chosen suitably for each plate type by experiments .This number must be in the approximate
42、 range of the number of characters in a VLP, we have two appropriate constraints for two types of Vietnamese plates:Preprocessing OCR Segmentation.
Images taken VLP Detection from camera License-patecharacters:4 ≤ N ≤ 8 with 1-row plate-candidates7 ≤ N ≤ 16 with 2-row plate-candidates
With N is
43、the number of cut-objects.The candidates that satisfied one of the two above constraints are selected as the final result.
In our system, we implemented two hoziontal cuts at 1/3and 2/3 of plate-candidate’s height. The average of number of cut objects will be calculated. This evaluation helps to id
44、entify the correct plate-candidates.
C. Segmentation
To correctly recognize characters, we have to segment a binary plate image to set of images which only contain one license character. These character images will be passed to the OCR module for recognizing. The common algorithm for this task i
45、s applying projections. However, in some cases, it does not work correctly. We will now describe our approach in segmentation by adding some enhancements to this method.
We use a horizontal projection to detect and segment rows in 2 row plates. Because binary plate images were adjustedtheir incli
46、ned angles to zero, the result of row segmentation is nearly perfect. The positions with minimum values of horizontal projection are the start or the end of a row in plate.
Different form row segmentation, character segmentation is more difficult due to many reasons such as stuck characters, scre
47、ws, and mud covered in plates. These noise things cause the character segmentation algorithm using vertical projection to have some mistakes. In some worst cases of bad quality plate images, a character can be segmented into two pieces.We apply several constraints of
ratio of the height to the widt
48、h of a character.
We search for the minimum values in the vertical projection and only the minimum positions which give cut pieces satisfied all predefined constraints are considered as the points for character segmentation. By this enhancement,we have achieved better results in this task. After
49、this step,we have a list of character candidates. Not all of the candidates are actually images of characters.
By that time, we can re-evaluate whether a plate candidate is a plate or not by checking the number of characters of candidates. In Vietnam, a plate contains only 7 or 8 characters . The f
50、inal plate candidates, together with their list of characters are passed to the OCR module for recognizing.
D. Hidden Markov Model for OCR
In this system, we use the HMM model for character
recognition. The features which we used in this model are the ratio of foreground pixels in a window.
We
51、 use a window with the size of 99, and scan this window in the image from left to right and top to bottom These windows can overlap each other by two thirds of their size. By this way, we have a feature vector which includes 196 values.
In the recognition module, we need to classify a character i
52、mage into one of 36 classes (26 alphabet letters: A, B, C…and 10 numeric characters: 0, 1, 2…).To train our model, we use training sets which were extracted from images of
VLPs.The number of samples for every class is about60.These samples were extracted from real VLP images with a little noise, so
53、 after well trained, the model can recognize exactly plates with the similar types of noise.In the last step, we use some specific rules of Vietnamese VLPs to improve accuracy.We learned that the third character in plate must be a letter, the fourth is sometimes a letter but usually a number, and th
54、e other positions are surely numbers.
IV. EMPIRICAL EVALUATION
Our system was evaluated with two sets of Vietnamese vehicles’ plates. Images were taken by a Sony DC350 digital camera, with size of 800x600 pixels, in different places and times. We use Microsoft Visual C++ 6.0, run on HP Workstati
55、on X2000 Pentium IV, 1.4 GHz, 512 MB RAM, Windows XP OS.
V. CONCLUSIONS AND FUTURE WORK
The system performs well on various types of Vietnamese VLP images, even on scratched, scaled plate images. In addition, it can deal with the cases of multiple plates in the same image, or different types of
56、vehicles such as motorbike
plates, car plates or truck plates. However, it still has a few errors when dealing with bad quality plates.
We are working on a number of algorithms in the preprocessing module. The purpose is to detect regions that are likely plate regions first and thus to reduce th
57、e computation cost of the VLP detection algorithm. In addition, we intend to combine a number of texture-based approachs, and machine learning methods to evaluate platecadidates. We believe these will improve the accuracy and the speed of the algorithm furthermore.
Index Terms—Vehicle License-Plate Recognition, Real-time
System, Hough Transform, Contour Algorithm.
電氣工程及其自動化 外文翻譯 外文文獻 英文文獻 建立一個自動車輛車牌識別系統(tǒng)
