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Vehicle License Plate (VLP) Recognition System. By German H. Flores and Gurpal Bhoot. Agenda. Introduction Goal and Motivation Image Segmentation Feature Extraction Classification Results/Conclusion Future Work. Introduction. Technological advancements in both software and hardware

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vehicle license plate vlp recognition system

Vehicle License Plate (VLP) Recognition System

By German H. Flores and GurpalBhoot

agenda
Agenda
  • Introduction
  • Goal and Motivation
  • Image Segmentation
  • Feature Extraction
  • Classification
  • Results/Conclusion
  • Future Work
introduction
Introduction
  • Technological advancements in both software and hardware
    • Better ways to capture, edit and analyze images
  • Safety and security of pedestrians and people in motorized vehicles
    • The large number of cars on the roads has increased the probability of an accident occurring
  • With a VLP system, the owner of a car can be easily identified and held responsible for their actions
assumptions
Assumptions
  • Ideal lighting Conditions
  • Non-white car
  • License Plate is in the same region
  • License Plates are similar sizes
  • Only California license plates after 1987
  • License Plates must be white with dark characters
  • Upper case letter O and 0 are the same
image segmentation

Binary Image

Image Segmentation
  • Convert the original image into a binary image
    • Threshold was chosen through testing

Binary Image

Resize Image

  • Shrink the image
    • Cut out the background
    • Leave only part of the image where license plate is most likely to appear
slide8

Image Segmentation

Windowing Method

  • Windowing Method used to find the license plate from the binary image
    • Send a window (m X n) through binary image, pixel by pixel

Resized Binary Image

slide9

Image Segmentation

Windowing Method

  • Find the license plate by number of white pixels
  • Below is the resulting image from applying the Window Method

Final Binary Image

slide10

Image Segmentation

Connected Component Algorithm

  • Used for separating license plate from the image
  • Finds the different objects
    • Finds the license plate by size and shape

Extracted License Plate

  • Then used for separating the letters and numbers
    • Finds each character and extracts them one by one
feature extraction
Feature Extraction
  • What features are important for a successful pattern classification?
    • Ex: Color, Area, Perimeter, mean, variance
  • Character

Recognition

feature extraction1
Feature Extraction

Area

Perimeter

Compressed and Normalized

Simple Compression

And Normalized Corners

Full Compression And

Normalized Corners

Perimeter of Contour

feature extraction2
Feature Extraction

(http://www.leewardpro.com/articles/licplatefonts/font-penitentiary.html)

Characters that have holes

  • Features:
  • Area
  • Perimeter
  • Perimeter of Contour
  • Number of Corners in simple
  • compressed Image
  • Number of Corners in full
  • compressed Image
  • Distance Image
  • Normalized Character Image

A B D O P Q R 0 6 8 9

Characters that do not have holes

C E F G H I J K L M N S T U V W X Y Z 1 2 3 4 5 7

feature extraction3
Feature Extraction

A corner can be defined as the intersection of two edges

  • Harris Corner Detection

A new Corner Matching Algorithm Based on Gradient. (Yu, Haliyan.,., RenCuihua., and QiaoXiaoling)

feature extraction5
Feature Extraction
  • Compute X and Y derivatives of the grayscale image

GxGy

  • Compute products of derivatives
  • Define at each pixel (x,y), the matrix
  • Compute the response at each pixel
  • Threshold on Value R

0s or negative numbers are the corners

feature extraction6
Feature Extraction

Character Features Extracted

From Image

Character Features

from Database

Correlation

Corr2()

conclusion overview
Conclusion/Overview

A B D O P Q R 0 6 8 9

C E F G H I J K L M N S T U V W X Y Z 1 2 3 4 5 7

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