Vehicle license plate vlp recognition system
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Vehicle License Plate (VLP) Recognition System PowerPoint PPT Presentation

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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

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Vehicle License Plate (VLP) Recognition System

By German H. Flores and GurpalBhoot


  • Introduction

  • Goal and Motivation

  • Image Segmentation

  • Feature Extraction

  • Classification

  • Results/Conclusion

  • Future Work


  • 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


Object Recognition Process

Process Flow


  • 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

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

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

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

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

Image Segmentation

Feature Extraction

  • What features are important for a successful pattern classification?

    • Ex: Color, Area, Perimeter, mean, variance

  • Character


Feature Extraction



Compressed and Normalized

Simple Compression

And Normalized Corners

Full Compression And

Normalized Corners

Perimeter of Contour

Feature Extraction


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 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 Extraction

Feature Extraction

  • Compute X and Y derivatives of the grayscale image


  • 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 Extraction

Character Features Extracted

From Image

Character Features

from Database







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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