License Plate Recognition

(LPR)

By: Mani Baghaei Fard

Preface

During recent years number of moving vehicles in roads and highways has been considerably increased

Hence, Intelligent transportation systems (ITSs) have been developed as a major tool for analyzing and also handling the moving vehicles in cities and roads

These systems attempt to facilitate the problem of identification of cars, via various techniques which mainly rely on automated (rather than manual) algorithms.

Image processing is one of these techniques

Unique property for identifying all vehicles is their license plate number.

Security control of restricted areas

parking management systems

traffic law enforcements

surveillance systems

Electronic toll collection

Some applications:

Difficulties: Poor image resolution usually because the plate is too far away but sometimes

resulting from the use of a low-quality camera.

Weather condition

complex background

plate deficiencies (damaged or dirty)

Supporting specific range of distances

Viewpoints

Blurry images, particularly motion blur

Poor lighting and low contrast due to overexposure, reflection or shadows

response time is another restriction in real time applications such as license plate tracking

Lack of coordination between countries or states which results in different design of the plates.

A license plate recognition (LPR) system mainly consists of three major parts

license plate detection (LPD) character segmentation and Optical Character Recognition(OCR)

The task of recognizing specific object (i.e. Car license plate here) in an image is one of the most difficult topics in the field of computer vision

There are many methods

license plate detection (LPD)

Edge-based techniques:

methods based on edge analysis combined with morphology operations achieved promising results . Presence of dark characters on the light background at license plate provides strong edges which can be used as a cue to detect the license plate.

Unfortunately, solely using edge information, fails the algorithm in complex scenes. Hence, combining edge information with other cues improves the detection rate.

Hough transform: attempts to find the rectangular shapes.

Advantages: useful in finding the boundary box of a license plate regardless of characters.

Disadvantages:

Not suitable for distorted or dirty plate

Computational complexity

is only suited for closed shut

Texture analysis: This approach takes the advantage of existing homogenous and frequent texture-like edges in

the plate region. Gabor filters have been one of the major tools for texture

analysis Using these filters, the process is independent of rotation

and scaling. It has the ability of studying images in an unlimited number of directions. But it is a time consuming

and complex method specially when applied to large images.

Fuzzy Logic-based Texture – based Neural networks Train and test techniques such as Adaboost And many others….

Definitely ,I am not going to details about all of them …!!!

Other methods:

An optimized Edge-based method

By observing license plates in images, two main features are noticed:

1) horizontal edges around a car plate are relatively strong and dominant.

2) density of vertical edges across a car plate are significant.

Methodology

These two important features and also low complexity for edge-based analysis motivate us to use edge information for the car plate detection.

So let`s do it step by step!

Step 1: RGB2GRAY

RGB

Gray

Step 2: Edging

Edging wrt to what axes is more efficient?

Horizontal Edges

Vertical Edges

Vertical & Horizontal Edges

Wise Idea

Estimating the location of plate with significant density of vertical edges

Roberts

Log

Zero cross

Canny

Prewitt

Sobel

Recall: Edging Methods

Can find Vertical and Horizontal edges seprately

By experience Sobel Preferred Cause of better response

Sobel Operator is a [1 2 1] filter

Result of Step 2

Step 3 :Enhancing Plate-like regions in order to have better

response in these areas

a major cause of failure for a plate detectionsystem is low quality of car image. In order to improve the quality of plate image I used a pre-processing algorithm which increases the image contrast at locations where might be a license plate.

variance of local intensity for constituting pixels of the license plate has a limited range and does not change dynamically. This function increases image

Zheng et al. method

Based on some experiments the local intensity variance for a plate region can be out of considered range 0–60.

method does not work well under severe illumination change.

Drawbacks:

He replaced the variance of image intensity with the density of vertical edges in Zheng`s method

Vahid Abolghasemi`s method

What logic has robust response in plate like

region?

Histogram Analyzing method

How about this example?

Mission failed again !!!

1-Reading image 2-RGB2gray 3-find out vertical edges using Sobel operator 4-Dilation along X axes 5-smoothing 6-using morphological tools to extract plate 7-enhancement and plate preparation for

OCR algorithm

I tried to find out another way