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Transcript of LICENSE PLATE EXTRACTION AND CHARACTER SEGMENTATION By HINA KOCHHAR NITI GOEL Supervisor...
![Page 1: LICENSE PLATE EXTRACTION AND CHARACTER SEGMENTATION By HINA KOCHHAR NITI GOEL Supervisor Dr. Rajeev Srivastava](https://reader036.fdocuments.net/reader036/viewer/2022082213/5a4d1b237f8b9ab0599962db/html5/thumbnails/1.jpg)
LICENSE PLATE EXTRACTION AND
CHARACTER SEGMENTATION
ByHINA KOCHHAR
NITI GOEL
SupervisorDr. Rajeev Srivastava
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Introduction
• License plate recognition is an image-processing technology to identify vehicles by their license plates.
• All vehicles have the identity displayed (license plate) so no additional transmitter or responder is required.
• These systems are country specific.
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System Definition
• The recognition of a license plate can be divided in three major parts :
Extraction of the license plate Isolation of the individual characters Character recognition
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Project Focus
• This project is concerned with the task of extracting the information needed to identify the characters.
• The input is an image of a vehicle assumed to be speeding.
• The goal is to extract the license plate and isolate the characters.
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Indian License Plates
• Licence plate number is issued by the district-level Regional Transport Office (RTO) of respective states.
• All license plates are supposed to follow certain guidelines.
• We assume license plates are in the correct format for our project.
SS DD AA NNNN
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Plate Extraction
• Assumptions made concerning the shape and appearance of the license plateThe license plate is a rectangular region of an easily
discernable colorThe width-height relationship of the license plate is
known in advanceThe orientation of the license plate is approximately
aligned with the axes
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Steps for Plate ExtractionDetection of white pixels using threshold method
Using a sample white image
Candidate area detection
Extracting license plate region from candidate area
Crop license plate
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Original Image
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Detection of white pixels using threshold value
Finding threshold using color sample• The license plates have a standard white color. • A 5 X 5 sample of this white color is taken. • The average of the matrix corresponding to this sample is
found.• This is used to detect white pixels from the image.
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Detecting white pixels in the image • For each pixel in the image, RGB values are compared
with the threshold.• If they lie in the range specified by the threshold, then
that pixel is assigned a value of 0.• The image obtained is subtracted from the original image
to get an image in which the license plate (in RGB format) is detected.
• This RGB image is then converted into grayscale image.
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Image after white pixel detection
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Image Containing License plate
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Candidate Area Detection
Dilation of Image• The basic effect of the operator on the image is to
gradually enlarge the boundaries of regions of foreground pixels.
• Thus areas of foreground pixels grow in size while holes within those regions become smaller.
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Finding connected components• Pixel connectivity is used to find all the connected
components in the image.• Finding all connected components in an image and
marking each of them with a distinctive label is called connected component labeling.
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Finding parameters of connected components• The connected component labeled image is used to get the area and
location of the components.• Minimum license plate area and height-to-width ratio have been
found. Minimum area = 2000 Minimum Height-to-Width ratio = 0.17 Maximum Height-to-Width ratio = 0.50• The connected components satisfying these values are found.• The connected component having the license plate will be at
maximum depth in the image.
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Candidate Region
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Extracting license plate region from candidate area
• The candidate area image is now used to find the license plate location by using the same steps as in candidate area detection.
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License plate extraction using same method used in candidate region selection
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Crop License Plate
• The sum of the lines and of the columns of the image is computed, obtaining one vector for each direction.
• For these two directions, first point respectively at the left and the right side of the vector that is superior or equal to the average is found, thus obtaining a rectangle to be used for cropping the plate.
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Figure showing sum of columns in improved image
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Figure showing sum of rows in improved image
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Extracted License Plate
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Conversion from gray-scale image to binary image and resizing
• Gray-scale image of license plate is converted into binary image
• Background of image is shown by binary value 0 and foreground are shown by binary value 1
• Binary image is resized to 50 X 250
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Binary Image of License plate
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Resized binary image of license plate (50 X 250)
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Character Segmentation
• The extracted plate is divided into nine images, each containing one isolated character.
• Since no color information is relevant, the image is converted to binary colors before any further processing.
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Peak-to-Valley Method
• A horizontal projection of a binary image of the plate reveals the exact character positions.
• Changes from valleys to peaks are searched by counting the number of white pixels per column in the projection.
• A change from a valley to a peak indicates the beginning of a character, and vice versa.
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Sum of the columns graph of binary resized image of license plate
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Images of segmented characters of license plate
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Conclusion
The results obtained are as follows :
TASK SUCCESS RATE
Plate Extraction 78.4%Character Isolation 88.9%
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Applications
• LPR applications have a wide range of applications which use the extracted plate number :ParkingAccess controlTollingStolen carsPublic parking etc.
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Future Work
• This project isolates the characters of the license plate.
• Further work in this project will include identifying these characters.
• Also the project can be generalized to identify all kinds of plates.
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References[1] A Licence Plate recognition tutorial,
www.licenseplaterecognition.com[2] Indian License plates,
http://en.wikipedia.org/wiki/Indian_licence_plates[3] Sandeep Phukan, "The scent of a scam”, The Week, January 12,
2003.[4] M. Shridhar, J.W.V. Miller, G. Houle, L. Bijnagte, “Recognition of
License Plate Images: Issues and Perspectives”, Proc. of the Fifth Intl. Conf. on Document Analysis and Recognition, pp. 17-20, Sept. 1999
[5] Pixel connectivity, http://www.cee.hw.ac.uk/hipr/html/connect.html[6] Connected component labeling,
http://www.cee.hw.ac.uk/hipr/html/label.html
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[7]Remus Brad, “License plate recognition system”, Computer Science Department, Lucian Blaga University, Sibiu, Romania
[8]Cohen, H., Bergman, G., Erez, J., 2002. Car License Plate Recognition, Project Report, Vision and Image Sequence Laboratory, Technion, Israel.
[9] Matlab 7.0 Help