Automatic Number Plate Recognition by Using Matlab

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Automatic Number Plate Recognition by Using Matlab P Sai Krishna Department of Electronics and Communication Engineering Andhra University College of Engineering A Visakhapatnam psaikrishna42 gmail com Abstract In this thesis work the text found on the vehicle plates is detected from the input image and this


P Sai Krishna,2 RELATED WORK, In this paper the text found on the vehicle plates is detected from the input image and this requires. the localization of number plate area in order to identify the characters present on it In literature. we can find many methods for number plate detection and recognition system The major. drawback is that how long it will take to compute and recognize the particular license plates This. is critical and most needed when it is applied to real time applications However there is always a. trade off between computational time and performance rate In order to achieve an accurate result. and increase the performance of the system more computational time is required For number. plate detection or localization techniques based on edge statistic and mathematical morphology. gives a very good result that uses vertical edge information to calculate the edge density of the. image followed by morphology methods such as dilation to extract the region of interest This. technique works well due to the fact that number plates always have high density of vertical. edges But in this method as unwanted edges in the background are also detected which leads to. confusion it is difficult to apply this method for number plates with complex background. Colour based techniques are proposed in this thesis work The draw back with this method is that. it performs well when the lighting condition is constant but when there is various illumination. condition its performance reduces But in real time application normally the images can be. obtained with various lighting illumination Furthermore the proposed technique is country. specific because each country will have different colour code for vehicle number plate Connected. Component Analysis CCA method is used to detect the number plate region CCA is useful for. simplifying the detection task since it labels binary image into several components based on their. connectivity Based on the problem one can decide on the selection of finding the connected. components using 4 adjacency or 8 adjacency of pixels connectivity Spatial measurement is a. measure of spatial characteristics of a connected component such as area orientation aspect ratio. etc and filtering is done to eliminate unrelated or unwanted components When Connected. Component Analysis is combined with spatial measurement and filtering produces better result in. number plate detection Automatic recognition of car license plate number became a very. important in our daily life because of the unlimited increase of cars and transportation systems. which make it impossible to be fully managed and monitored by humans examples are so many. like traffic monitoring tracking stolen cars managing parking toll red light violation. enforcement border and customs checkpoints Yet it s a very challenging problem due to the. diversity of plate formats different scales rotations and non uniform illumination conditions. during image acquisition, This paper presents an approach using simple but efficient morphological operations filtering and. finding connected components for localization of Indian number plates The algorithm has been. tested on 20 samples and is found to extract both alphabets and numbers from vehicle license. plates images with an accuracy of 90 for four wheeler license plates of different regions in. different climatic conditions HD number plates are also identified by resizing them to aspect. 3 RELATED DEFINITIONS, A detection algorithm that employs mathematical morphology structuring element median. filtering edge detection to detect the license plate is detailed below. 3 1 Mathematical Morphology, Mathematical Morphology is set theoretic method for analysing the image and extracting image. components that are useful in the shape representation and extraction of geometrical structure. They are used to detect boundaries of objects their skeletons and their convex hulls These are. the basic operations that has to be carried over for any image pre and post processing techniques. that include edge thinning thickening region filling pruning etc The following operations form. the basis of mathematical morphology, Dilation will cause objects to grow in size as it will exchange every pixel value with the.
maximum value within a 3x3 window size around the pixel That is it adds pixels to the. boundaries of objects in an image The process may be repeated to create larger effects The size. International Journal of Innovative Research in Electronics and Communications IJIREC Page 2. Automatic Number Plate Recognition By Using Matlab. and shape of the structuring element decides the number of elements to be added to the image. under processing, Erosion works the same way except that it will cause objects to decrease because each pixel value. is exchanged with the minimum value within a 3x3 window size around the pixel That is it. removes pixels from the boundaries of objects in an image The size and shape of the structuring. element decides the number of elements to be removed from the image under processing. Opening is an important morphological operator It is defined as erosion followed by dilation. Erosion tries to eliminate some of the foreground bright pixels from the edges of regions of. foreground pixels The disadvantage is that it will remove all regions of foreground pixels. indiscriminately Opening gets around this by performing both erosion and dilation on the image. Closing is similar in some ways to dilation in that it tends to enlarge the boundaries of foreground. bright regions in an image and shrink background colour holes in such regions but is less. destructive of the original boundary shape Closing is defined as dilation followed by erosion. The effect of the operator is to preserve background regions that have a similar shape to this. structuring element or that can completely contain the structuring element while eliminating all. other regions of background pixels,3 2 Structuring Elements. In order to carry over the dilation and erosion operations on images the structuring element are. used A structuring element is a matrix with m n size The values in this matrix are of binary. value that is either a 1 or 0 The pixels with values of 1 next to each other are called the. neighbourhood pixels, In a morphological operation the origin of the structuring element is compared with every pixel. along with its neighbours in the input image and translated to each pixel position in the. corresponding output image The outcome of this comparison depends upon the type of. morphological operator and size of structural element used Sample structuring elements are given. in figure 2,Figure 2 Examples of simple structuring elements. The red colour in each matrix represents the origin This paper uses a 2 4 structuring element. 3 3 Median Filter, The median filter is a non linear filtering technique used to remove noise from image under.
consideration While it helps in removing the impulse noise it preserves the edges As the impulse. International Journal of Innovative Research in Electronics and Communications IJIREC Page 3. P Sai Krishna, noise spikes are much brighter than their neighbouring pixels they are generally placed in the. extreme top or bottom end of the brightness ranking while analysing the neighbourhood of input. pixels As a consequence these extremes values with noise which lie far away from the median. value are removed by the filter which leads to dramatic reduction of noises from the image. Repeated application of median filter make the image with uniform regions that are very effective. when classified for segmentation Because of its nonlinearity it is unsuitable for common. optimization techniques As a matter of fact median filter is a statistical non linear filter that is. often described in the spatial domain A median filter also smoothens the image by utilizing the. median of neighbourhood In this experiment a 2 4 median filter is used mainly because this. filter is more effective than convolution when the goal is to simultaneously reduce noise and. preserve edges It behaves like low pass filtering in smoothening and reducing the noise in the. image while preserving discontinuities and smooth the pixels whose values differ significantly. from their surroundings without affecting the other pixels which is lacking in low pass filters. 4 METHODOLOGY ADOPTED, Generally the text in number plates are written with contrast background and foreground like. black letters on white background and black letters on yellow background and based on this. property of text a localization technique has been proposed in this paper The work is divided into. three major parts namely pre processing text localization and extraction and text non text. classification,4 1 Pre processing, In the pre processing step the coloured input image is converted to grey scale image The image. is then binarised A binarised image is a must for doing all morphological operations like opening. closing thinning skeletonization region filling etc To this binarised image median filtering is. applied to remove any noise if presents Edge detection algorithm is applied on the resultant. image to extract the edges,4 2 Text localization and Extraction. In this phase the morphological dilation operation is performed on the edge image obtained from. the previous step Since texts are normally aligned in the horizontal direction a 2x4 rectangular. structuring element is used All Connected Components are then extracted. 4 3 Text non text classification, The extracted components from the above step contains both text and non text components They.
are separated and eliminated by a two way process First the initial bounding boxes are drawn for. all objects figure 3 The required texts in the connected components are extracted and placed in. a jpeg file, International Journal of Innovative Research in Electronics and Communications IJIREC Page 4. Automatic Number Plate Recognition By Using Matlab. The flowchart explaining the algorithm is given in figure 4. Figure 4 The proposed algorithm for extraction of number plates. 5 RESULTS AND ANALYSIS, The tests were conducted on 20 images taken with the help of 10mega pixels digital camera and. MATLAB R2013b software was used for the experiment About 90 of the number plates were. localized correctly and 10 images resulted in the localization of number plates along with. unwanted non candidate regions because of the damage in the number plates Except for the. unwanted regions the algorithm works robust under different illumination and brightness. The following table 1 shows the extracted text non text from some of the vehicle number plates. and their authentication, International Journal of Innovative Research in Electronics and Communications IJIREC Page 5. P Sai Krishna,INPUT IMAGE OUTPUT OBSERVED,6 CONCLUSION. This article proposes a text localization and extraction technique from vehicle number plates The. suggested method is tested with various types of vehicles with different background and different. climatic condition Differentiation of characters from numbers had been done in our project. Some characters and numbers have similar shape and it becomes difficult to compare with. template In this case we used a condition to eliminate confusion between numbers and characters. Usually in Indian number plates we observe alphabets in 1 2 4 5 positions Hence in this positions. segmented characters correlates with templates and when maximum co relation occurs in that. position alphabet is recognized Except in these positions all the other must be numbers Then. template in data base co relates with 2 3 6 7 8 9 10 positions and when maximum correlation is. observed then it must be a number Hence confusion between alphabet and numbers is eliminated. HD number plates can also be recognized as they are reduced to default aspect ratio In our. project stolen cars can be identified by an array in database of Matlab We have tested 20 number. plates of different vehicles in different climatic conditions and we got 18 successful outputs. Success rate of our project is 90,7 FUTURE SCOPE, Due to the varying characteristics of the license plate among countries regions further research is.
still needed in this area Different filtering techniques can be introduced to reduce the noise The. integration of multiple algorithms for image segmentation in addition to Sobel edge detection and. binary image segmentation can be considered In future the extraction of multi plates high. definition plate processing multi style plates can be done. International Journal of Innovative Research in Electronics and Communications IJIREC Page 6. Automatic Number Plate Recognition By Using Matlab. REFERENCES, 1 http en wikipedia org wiki Vehicle registration plates of India. 2 Dai Yan L J Ma Hongqing L Langang 2001 A high performance license plate recognition. system based on the web technique Proceedings of International Conference on Intelligent. Transportation Systems 325 329, 3 Paolo Comelli M N G Paolo Ferragina F Stabile 1995 Optical recognition of motor. vehicle License plates IEEE Transactions on Vehicular Technology 44 790 799. 4 Oz C F Ercal 2005 A practical license plate recognition system for real time environments. 3512 Springer Verlag, 5 Jun Kong Y L Xinyue Liu X Zhou 2005 A novel license plate localization method based. on Textural feature analysis Proceedings on IEEE International Symposium on Signal. Processing and Information Technology 275 279, 6 Duan T D T L H Du N V Hoang 2005 Building an automatic vehicle license plate. recognition system Proceedings of International Conference on Computer Science 59 63. 7 Vladimir Shapiro Dimo Dimov Stefan Bonchev Veselin Velichkov and Georgi Gluhchev. Adaptive License Plate Image Extraction Proceedings of the International Conference on. Computer Systems and Technologies CompSysTech 2003. 8 Ching Tang Hsieh Y S J K M Hung 2005 Multiple license plate detection for complex. background Proceedings of International Conference on Advanced Information Networking. and Applications 2 389 392,AUTHOR S BIOGRAPHY, P Sai Krishna aged21 is a 4th year student in Andhra University College of.
Engineering A Visakhapatnam Andhra Pradesh His interest is in Image. Processing, International Journal of Innovative Research in Electronics and Communications IJIREC Page 7.

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