An automatic license plate recognition system (LPRS) uses computer vision techniques to detect, recognize, and identify vehicle license plates. It consists of three main modules: license plate detection using image preprocessing and morphological operations, character segmentation through binarization and segmentation, and optical character recognition using template matching. LPRS has applications in traffic monitoring, electronic toll collection, and surveillance. Previous work has investigated various methods for each stage, such as edge statistics for plate localization and projection/clustering for character segmentation.

1. Automatic License Plate
Recognition System

2. Introduction
 A License Plate Recognition System (LPRS) is a
system to automatically detect, recognize and
identify a vehicle plate.
 Vehicle license plate (VLP) constitutes an
unambiguous identifier of a vehicle participating in
road traffic. Reading a license plate is the first step
in determining the identities of parties involved in
traffic incidents.

3. 
License-Plate Recognition System consists of
three main modules:
 License plate detection,
 character segmentation and
 Optical Character Recognition (OCR).

4. Flow chart
Start Input Image
License Plate
Extraction
Character
Segmentation
Character
Identification
Display
Plate
Number
End

5. License Plate Detection

Preprocessing : The first step is to identify the regions in the
image that contain the intensity of RGB corresponding to the color
yellow

Morphological operation : These are Non-linear filters,
with the function of restraining noises, extracting features and
segmenting images etc

Horizontal segmentation : It will give the width and the
x coordinates of the potentially candidates regions.

6. 
Vertical segmentation : It will give the Height and the y
coordinates of the potentially candidates regions

Identifying License Plate : Two features are defined
and extracted in order to decide if a candidate region contains a
license plate or not , these features are:
1. Aspect ratio
2. Edge Density

7. Character segmentation

Conversion to gray scale.

Binarization.

Horizontal segmentation.

Vertical Segmentation.

8. Optical Character
Recognition(OCR)

Training : The program is first trained with a set of sample
images for each of the characters to extract the important features
based on which the recognition operation would be performed.

The program must be trained on a set of 10 characters with 10
samples of each. The training algorithm involves the following steps:
Preprocessing.
Template creation.

9. 
Recognition :
Preprocessing.
Template matching : Calculate the matching score of
the segmented character from the templates of the character
stored by the following algorithm.

Compare the pixel values of the matrix of segmented character
and the template matrix, and for every match we add 1 to the
matching score and for every mis-match we decrement 1. This is
done for all 225 pixels. The match score is generated for every
template and the one which gives the highest score is taken to be
the recognized character

10. Applications

computerized road traffic monitoring systems

electronic fee collection solutions,

surveillance devices

safety supervision systems

11. Previous Work
 Many difference solutions have already been
proposed for each stage of recognition
 Plate localization
– Use edge statistics to locate the plate
– Fuzzy clustering algorithms

12. Previous Work
 Character Segmentation
– Vertical/horizontal projection
– Adaptive Clustering
 Optical Character Recognition
– Template matching
– Neural network
– Feature analysis

13. References

Automatic Vehicle License-Plate Recognition System By Deepak Kumar
Gupta-Y6154 and Siddhartha Kandoi-Y6472

Combining Hough Transform and Contour Algorithm for detecting
Vehicles. License-Plates - Tran Duc Duan, Duong Anh Duc, Tran Le
Hong Du – October 2004

Building an Automatic Vehicle License-Plate Recognition System - Tran
Duc Duan, Tran Le Hong Du, Tran Vinh Phuoc, Nguyen Viet Hoang –
Febraury 2005

A High Accurate Macau License Plate Recognition System -
CheokMan , Kengchung - 2008

Rectangular Object Tracking Based on Standard Hough Transform -
Thuy Tuong Nguyen, Xuan Dai Pham and Jae Wook Jeon, - February,
2009

A New Algorithm for Character Segmentation of License Plate - Yungang
Zhang Changshui Zhang - June 2003

Mean Shift for Accurate Number Plate Detection - Wenjing Jia, Huaifeng
Zhang, and Xiangjian He - July 2005