License Plate Recognition

emerge Trends in computing machine erudition and In normalation engineering -2012(ETCSIT2012) proceeding create in internationalist diary of data dishor Applications (IJCA) Automatic Vehicle appointment utilise licence dental shield wisdom for Indian Vehicles Sandra Sivanandan disunite of Computer applied science K. K. Wagh Institute Of design Education & Research, Hirabai Haridas Vidyanagari Amrut-Dham, Panchavati, Nashik-422003 University of Pune, Maharashtra Ashwini Dhanait plane section of Computer engine room K. K.Wagh Institute Of engineering science Education & Research, Hirabai Haridas Vidyanagari Amrut-Dham, Panchavati, Nashik-422003 University of Pune, Maharashtra Yogita Dhepale Department of Computer Engineering K. K. Wagh Institute Of Engineering Education & Research, Hirabai Haridas Vidyanagari Amrut-Dham, Panchavati, Nashik-422003. Yasmin Saiyyad Department of Computer Engineering K. K. Wagh Institute Of Engineering Education & Research, Hirabai Hari das Vidyanagari Amrut-Dham, Panchavati, Nashik-422003. ABSTRACT In this study, a smart and plain algorithmic programic rule is presented for vehicles endorse headquarters realisation corpse.The proposed algorithm consists of three major move Extraction of dwelling region, air division of consultations and perception of nursing home parts. For extracting the scale of measurement region butt contracting and morphologic trading operations atomic anatomy 18 used. In sectionalization part see credit line algorithm is used. voice breakdown for Devanagari Number dentures is also presented. Optical constituent recognition technique is used for the eccentric recognition. The objective is to design an efficient self-locking authorized vehicle identification arrangement by using the vehicle number home base.Here we atomic number 18 presenting a smart and simple algorithm for vehicles demonstrate plate recognition system for Indian Vehicles. In this study, the proposed algorithm is based on extraction of plate region, segmentation of plate extensions and recognition of characters. In India we find plates having Devanagari fonts as well (though according to rules it is non allowed). record extraction for Devanagari font is slightly contrastive as compargond to English font because of the oral sex line (shirorekha). We propose algorithm for character extraction for Devanagari font. The recognized plate an be then compared with police hotlist database to identify stolen vehicles. The idea is organized as follows Section II provides an overview of the overall system. Extracting the plate region is explained in Section III. Section IV gives the segmentation of singular plate characters. Section V deals with recognition of characters using optical character recognition based on statistical based template matching algorithm which uses correlation and section VI deals with hindrance of plate according to Indian rules. The study conclude s with Section VII. KeywordsDevanagari, sharpness detection, License plate recognition, Optical character recognition, segmentation. 1. INTRODUCTION License plate recognition (LPR) is a form of Automatic Vehicle Identification. It is an symbol bear upon technology used to identify vehicles by scarce their license plates. Real prison term LPR plays a major role in automatic monitoring of traffic rules and maintaining justness enforcement on public roads. The LPR systems signifi guttert advantage is that the system can keep an envision record of the vehicle which is useful in assign to conjure crime and fraud (an regard is worth a thousand words).Early LPR systems suffered from a low recognition rate, lower than requisite by practical systems. The external set up (sun and headlights, bad plates, wide number of plate types) and the limited level of the recognition software system and vision computer hardware yielded low feel systems. However, recent improvements in the s oftware and hardware have made the LPR systems much much reliable and wide spread. 23 Emerging Trends in Computer Science and learning Technology -2012(ETCSIT2012) Proceedings published in International Journal of Computer Applications (IJCA) in night condition, contrast enhancement is alpha before further processing 1. . structure OF LPR SYSTEM Fig. 1) Original come across Fig. 2) colourize Scale Image Flowchart of Proposed dust The algorithm proposed in this paper is intentional to recognize license plates of vehicles automatically. Input of the system is the word picture of a vehicle captured by a camera. The captured design taken from 3-5 meters pop outdoor(a) is foremost converted to colour scale. We employ vertical march detection algorithm and morphological operation i. e. open and close down for plate extraction. After gulling morphological operations render is drooled out to get recognise plate region. Plate region is cropped.Row segmentation separat es row in plate and tower separation separates characters from row. Finally recognition part OCR recognizes the characters giving the result as the plate number in ASCII format. The result in ASCII format is can be corroborate on the initiation of rules followed in India. Fig. 3) colorise motion-picture show after contrast enhancement 3. 2 upright Edge sensing Before applying edge detection median(a) value filter is to be applied to control for removing noise. The main idea of median filter is to run through the signal, entry by entry, replacing each entry with the median of neighboring entries.Such noise reduction is a typical preprocessing step to improve the results of subsequently processing (edge detection) 2. 3. EXTRACTION OF PLATE locality Plate Extraction is done in following steps 3. 1 transform picture show to rusty Scale 3. 2 Apply Vertical Edge detection 3. 3 candidate Plate arena Detection ? morphologically Close image ? Fill holes in image ? Morphologica lly Open image 3. 3 Filtration of non Plate region 3. 1 Conversion To Gray Scale This is pre-processing step for plate extraction. We apply Formula I( i, j) = 0. 114*A( i, j,1) + 0. 587*A(i, j, 2) + 0. 99* A(i, j,3) where, I(i,j) is the array of gray image, A(i,j,1), A(i,j,2), A(i,j,3) are the R,G,B value of victor image respectively. Sometimes the image whitethorn be too dark, arrest blur, thereby devising the task of extracting the license plate difficult. In order to recognize the license plate even In ascending order of value 0, 2, 3, 3, 4, 6, 10, 15, 97. Center value (previously 97) is replaced by the median of all nine values (4). Edge detection is performed on the effrontery image, which aims at identifying points in digital image at which image brightness changes crisply or, much formally, has discontinuities.There mainly exists several edge detection methods (Sobel, Prewitt, Roberts, Canny). We use here Sobel performer for vertical edge detection. If we define A as t he source image, and Gx and Gy are two images which at each point contain the horizontal and vertical derivative approximations, the computations are as follows 24 Emerging Trends in Computer Science and Information Technology -2012(ETCSIT2012) Proceedings published in International Journal of Computer Applications (IJCA) Where * is 2D spin operation. Fig. 5) Closed Image Fig. 4) Sobel Vertical Edge detection Fig. 6) Filled Image 3. Candidate Plate Area Detection A morphological operator is applied to the image for specifying the plate location. We build a morphological operator that is sensitive to a special stamp in the input image. In our system orthogonal box is utilize as a structural portion to detect the car plates. In numeric sound structure structuring element are stand for as matrices. Structuring element is a property of received structure and causes to measure the shape of an image and is used to carry out other image processing operations 4. Typical rectangu lar structuring element is shown in figure. Fig. ) Opened Image 3. 4 Filtration Of Non Plate Region After identify the ROI, image is then filtered using following filtering techniques. initiative find the connected components in image. The first technique involves removing of all white patches which has more(prenominal) or little area than the threshold. For vitrine components having area 2000 or 20000 are eliminated. development Bounding Box method, draw Bounding Box near components and fill the image. According to the height values, for instance, only the objects with a height greater than Tmin_h and less than Tmax_h are retained, and eliminate the other objects.After that, if the breadth values of the retained objects are greater than Tmin_w and less than Tmax_w, the objects are retained otherwise, the objects are removed, and so on. Where Tmin_h Minimum height of the object. Tmax_h supreme height of the object. Tmin_w Minimum width of the object. Tmax_w level best width of the object 6. After filtering plate region is cropped by searching for the first and last white pixels starting from covering left corner of an image. Plate is cropped from original image after getting coordinates. Using two basic operation of morphology (erosion and dilation), theory and shutting of image is done.The opening of A by B is obtained by the erosion of A by B, followed by dilation of the resulting image by B. The occlusion of A by B is obtained by the dilation of A by B, followed by erosion of the resulting structure by B. For closing image 10*20 rectangular structuring element is used. After closing image we have to fill the holes in this image. A hole is a set of background signal pixels that cannot be reached by filling in the background from the edge of the image 3. and then image is opened using 5*10 rectangular structural element. set are determined according to the coat of the image.Here we have used 1280X980 resolution images. 25 Emerging Trends in Computer Science and Information Technology -2012(ETCSIT2012) Proceedings published in International Journal of Computer Applications (IJCA) 4. SEGMENTATION OF PLATE CHARACTERS Before applying the OCR, the individual lines in the text are illogical using line separation process and individual characters from separated lines. Steps for Character Segmentation 4. 1 Binarization of Plate image 4. 2 conk out Line algorithmic rule for row segmentation 4. 3 Vertical Projection for column segmentationFig 6) Filtered Image on basis of area Fig. 7) Bounding Box and modify image 4. 1 Binarization Of Plate Image Binarize the plate image. Threshold for binarization must be such that characters are displayed well. For that we take middling of all pixel values in plate image and calculate threshold. Fig. 10) Binarized image Fig. 8) Image after filtration on basis of height &width of objects 4. 2 Scan Line Algorithm The scan line algorithm is based on the feature that there is alteration from 1 to 0 and 0 to 1 transition in character region in a binary image.Thus the native number of transition in character region is more than the total number of transition in other region. There are at least seven characters in license plate region and every character has more than two Jumps7. We can choose twelve as the threshold value. If the total number of transitions in a certain line is greater than twelve, this line may be in character region. Otherwise, it is not in character region. Algorithm 1) allow H be height and W be Width of Plate image. 2) for(i=H/2 to 0) Count no of transitions ie 0 to 1 and 1 to 0 in cnt if cnt