License Plate Recognition of A Vehicle using MATLAB Under the guidance of Prudhvi Raj By Chetana {Y07EI021} C.Madhuri {Y07EI019} Vinod {Y07EI045} M.Praveen Kumar {Y07EI055}

Contents: Aim Introduction Other Names Elements of LPR Algorithms Operation

Aim: Recognition of license number of a vehicle by the detection of its license plate from the vehicle image captured by the camera using MATLAB programming.

Introduction: What is LPR……………….? LPR

License Plate Recognition {LPR} LPR {License Plate Recognition} is an image-processing technology used to identify vehicles by their license plates. This technology is used in various security and traffic applications.

Also Called As: Automatic Vehicle Identification (AVI) Car Plate Recognition (CPR) Automatic Number Plate Recognition (ANPR) Car Plate Reader (CPR) Optical Character Recognition (OCR) for Cars

Elements of LPR License-Plate Recognition System consists of three main modules: Extraction of plate region Character segmentation Optical Character Recognition

Algorithms: Extraction of plate region Smearing Algorithm Edge Detection Algorithm Character segmentation Filtering Algorithm Morphological Algorithm Optical Character Recognition Statistical Algorithm Template Matching

Operation: Image captured from the camera is first converted to the binary image {only black and white} consisting of only 1’s and 0’s by thresholding the pixel values of 0 (black) for all pixels in the input image with luminance less than threshold value and 1 (white) for all other pixels.

Extraction of Plate Region To find the plate region, firstly smearing algorithm is used. Smearing Algorithm: It is a method of extraction of text areas on a mixed image. The image is processed along vertical and horizontal runs. If the number of white pixels is less than a desired threshold or greater than any other desired threshold, white pixels are converted to black.

The image is converted into binary coding & smearing is applied. Fig{1}: Original Image The image is converted into binary coding & smearing is applied. Fig{2}: After smearing algorithm Fig{1} Fig{2}

After smearing, a morphological operation, dilation, is applied to the image for specifying the plate location. However, there may be more than one candidate region for plate location. To find the exact region and eliminate the other regions, some criteria tests are applied to the image by smearing and filtering operation. The processed image after this stage is as shown in Figure 2(a) and image involving only plate is shown in Figure 2(b).

After obtaining plate location, region involving only plate is cut giving the plate as shown in Figure 3.

Elements of typical LPR systems Cameras Illumination Frame grabber Computer Software Hardware Database

Image Acquisition License Plate Extraction Segmentation Recognition

Commercial Products IMPS (Integrated Multi-Pass System) Perceptics Vehicle Identification System for Parking Areas (VISPA) Hi-Tech Solution

Applications of LPR Systems Law Enforcement Parking Automatic Toll Gates Border Crossing Homeland Security

Example of one application:

Techniques: automatic number-plate recognition using optical character recognition techniques knowledge-guieded boundary following and template matching for automatic vehicle identification. bidirectional associative memories (BAM) neural network for number plate reading. vertical edge using Hough transform (HT) for extracting the license plate neural network for color extraction and a template matching to recognize characters. genetic algorithm based segmentation to extract the plate region