Skip to content Skip to navigation Skip to collection information

OpenStax-CNX

You are here: Home » Content » ELEC 301 Group Project- License Plate Extraction (LiPE) » Image Processing - License Plate Localization and Letters Extraction

Navigation

Table of Contents

Lenses

What is a lens?

Definition of a lens

Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

This content is ...

Affiliated with (What does "Affiliated with" mean?)

This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.
  • Rice University ELEC 301 Projects

    This module is included inLens: Rice University ELEC 301 Project Lens
    By: Rice University ELEC 301As a part of collection: "ELEC 301 Projects Fall 2009"

    Click the "Rice University ELEC 301 Projects" link to see all content affiliated with them.

Also in these lenses

  • Lens for Engineering

    This module is included inLens: Lens for Engineering
    By: Sidney BurrusAs a part of collection: "ELEC 301 Projects Fall 2009"

    Click the "Lens for Engineering" link to see all content selected in this lens.

Recently Viewed

This feature requires Javascript to be enabled.
 

Image Processing - License Plate Localization and Letters Extraction

Module by: Cynthia Sung, Chinwei Hu, Kyle Li, Lei Cao. E-mail the authors

Summary: ELEC 301 Group Project: LiPE How to find the license plate letters and numbers in an image of a car.

The general method we used for extracting the license plate letters out of a picture was:

Figure 1: Overall method for finding license plate letters/numbers.
Figure 1 (graphics1.png)
  1. Compression and Cropping: decreases the size of the photo and and blacks out areas that definitely do not contain license plate.
  2. License Plate Localization: determines the location of the plate in the photo
  3. Letter Extraction: searches within the plate for the plate letters/numbers and copies them out of the photo.

Following is an explanation of the individual sections. Included are also images showing the effects of each section on the following picture:

Figure 2: Original image.
Figure 2 (graphics2.jpg)

Compression and Cropping

In order to decrease image processing time, we first compress all pictures to a standard size and black out all areas that are definitely not license plates. The size we chose was 640px by 480px, the smallest size at which we could still reasonably read the license plates.

To determine which areas were definitely not license plates, we realized that the typical Texas plate contained red, blue, and white as major colors. Therefore, we focused on these two colors, making our cropping algorithm:

  1. Separate the JPEG picture into its three layers of red, green, and blue.
  2. Consider the area around a blue pixel, and black it out if the density of red is lower than a certain threshold value.

Figure 3: Compressed and cropped image (Note the black areas around the right and bottom of the photo).
Figure 3 (graphics3.jpg)

License Plate Localization

Once we determined which areas possibly contained license plates, we looked in those areas for the plates themselves. We determined that most plates contain dark letters on light backgrounds and so looked for areas of high contrast.

Our final algorithm looked as follows:

  1. Turn the cropped photo into black-and-white for easier differentiation between dark and light spots.
  2. Filter the image to remove noise (single-pixel white spots).
  3. Locate the license plate position by scanning the photo vertically. We expect a row running through the license plate row to have a maximum number of individual dark spots, or "clusters." Therefore, we find and store the two rows in the image with that contain the most clusters.
  4. To find the horizontal position of the plate, scan the picture horizontally by moving a square window from left to right and counting the number of clusters inside. The final position of the license plate is square that contains the greatest number of clusters. If any two squares contain the same number of clusters, the two are merged together.
Figure 4: Close-up of the license plate as determined by the algorithm.
Figure 4 (graphics4.jpg)

Letter Extraction

To find the letters on a license plate, we first determined some identifying characteristics:

  1. Usually, and always on Texas plates, the letters of the plate are dark on a light background.
  2. The letters are uniform in height
  3. The letters all occur in approximately the same area.
  4. There are usually between 3 and 7 letters on a plate.

These characteristics give us the form for our letter extracting algorithm.

  1. From the plate-locating algorithm, we have a small 200px by 200px image that contains the car's license plate.
  2. We convert the image into grayscale for easier processing.
  3. We locate all the dark (low intensity) spots in the picture that are surrounded by light (high intensity) spots. We determine the separation between these dark spots and give each individual spot a label.
  4. Compare the sizes of the spots and look for about six that have the same height. These six spots are the letters on the plate.
  5. Save the pixels that make up the letters into their individual matrices.

We tried this algorithm and found that it worked for all images for which the plate-locating algorithm returned an image containing the entire plate.

Figure 5: Letters extracted from the photo.
Figure 5 (graphics5.png)

Collection Navigation

Content actions

Download:

Collection as:

PDF | EPUB (?)

What is an EPUB file?

EPUB is an electronic book format that can be read on a variety of mobile devices.

Downloading to a reading device

For detailed instructions on how to download this content's EPUB to your specific device, click the "(?)" link.

| More downloads ...

Module as:

PDF | EPUB (?)

What is an EPUB file?

EPUB is an electronic book format that can be read on a variety of mobile devices.

Downloading to a reading device

For detailed instructions on how to download this content's EPUB to your specific device, click the "(?)" link.

| More downloads ...

Add:

Collection to:

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

Definition of a lens

Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

| External bookmarks

Module to:

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

Definition of a lens

Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

| External bookmarks