A low pre processing system for low quality QR code

WELCOME

A low pre-processing system for

restoring low quality QR code

images

Presenting by-

Shameed C M

Roll No:49

Reg No:12123852

College of Engineering Attingal

Contents

Introduction

What is a QR code ?

History of QR code

Bar code VS QR code

Inside QR codes

What can a QR code do?

Application of QR code

Pre-processing

QR code decoding

Conclusion

References

Introduction

2D barcodes are the mainstream of recognition technologies used

for mobile marketing.

Easily conversion of physical printed data to digital data

2D barcodes are called two-dimensional because their information

is encoded in two directions, both horizontally and vertically.

WHAT IS A QR CODE ?

QR code: Quick Response code

Is a 2-D bar code

Used to encode information(URLs, automated SmS, text data) in two dimensional space.

HISTORY

Created by a Japanese

company- Toyota subsidiary

Denso-wave in 1994.

Used to track parts during vehicle

manufacture

Barcodes V/S QR Codes

Stores data in two dimensions unlike the 1-D barcodes.

High storage capacity in less space.

Can be scanned by any smart phone with the app installed.

Inside QR codes

Finder Patterns: Finds if the given code is a QR Code

Timing Pattern: Tracks the timing of the incoming code

Separators: Separates data from the patterns

Alignment Pattern: Tells about initiation & Separation of data.

Dark Module: A black module placed beside the bottom left finder pattern.

Cont…

Many Alignment patterns

Long timing pattern

Versions of QR code

Version 1 (21×21)

Version 2 (25×25)

Version 3 (29×29)

Versions of QR code cont…

Version 4 (33×33)

Version 10 (57×57)

Version 25 (117×117 enlarged to

640x640)

Versions of QR code cont…

Version 40 (177×177)

View a web page

Dial telephone number

Send a template text message

Send an e-mail

View a message or special offer

Download contact details (vcard)

View a google maps location

View a social media profile

Take a snap and view web page

View a web page

Send an e-mail

Capture and call

View a web page

Send an e-mail

Take a snap and send a template

message

View a web page

Send an e-mail

snap and send a default mail

View a web page

Send an e-mail

Take a snap

and see the

message

View a web page

Send an e-mail

Capture and save contact

View a web page

Send an e-mail

Capture and recognize the exact place

View a web page

Send an e-mail

Snap and see the media profile

Applications

Conference/event displays

Print advertisements

Business cards

Brochures , posters, & flyers

Post cards & mail

Applications

Business cards

Post cards & mail

Applications

Business cards

Post cards & mail

Applications

Business cards

Post cards & mail

Applications

Business cards

Post cards & mail

Pre-processing

Capturing image

Contrast enhancement

QR code extraction

Deblurring the code

Code alignment : corner and pattern detection and

transformation

Scanning the code

Pre-processing

Capturing image

QR code extraction

Deblurring the QR code

Pattern detection

Scanning the code

The images are captured by a 3 Megapixel-sensor

continuously refreshed on the screen to provide the user

with some feedback during the image capture process

Pre-processing

Capturing image

QR code extraction

Pattern detection

Scanning the code

To balance environment light effect

Histogram equalization

It is a method in image processing of contrast adjustment

using the image's histogram.

Employs a monotonic, non-linear mapping which re-assigns

the intensity values of pixels in the input image such that the

output image contains a uniform distribution of intensities

Pre-processing

Capturing image

QR code extraction

Pattern detection

Scanning the code

Cropping out external surroundings

Image binarisation

Edge detection

Morphological operations

Quality index

QR code extraction

1. Image binarisation

Binarization image by multi-level

thresholding method.

Captured imageThe simplest way to use image binarisation is to

choose a threshold value, and classify all pixels

with values above this threshold as white, and all

other pixels as black.

There are only two levels of intensity in the binary

image. We consider the pixels that have low

intensity is an image pixel where high intensity as

background pixel.

QR code extraction cont…

2. Edge detection

I. Smoothing

II. Finding gradients

III. Non maximum suppression

IV. Double thresholding

V. Edge tracking by hysteresis

2. Edge detection

I. SmoothingII. Finding gradients

Blurring the image to avoid noise.

2. Edge detection

I. Smoothing

II. Finding gradientsIII. Non maximum suppression

The edges should be marked where the gradients

of the image has large magnitudes.

The algorithm basically finds edges where the

greyscale intensity of the image changes the most.

These areas are found by determining gradients of

the image.

2. Edge detection

I. Smoothing

III. Non maximum suppressionIV. Double thresholding

Only local maxima should be marked as edges.

The purpose of this step is to convert the “blurred”

edges in the image of the gradient magnitudes to

“sharp” edges.

Basically this is done by preserving all local

maxima in the gradient image and deleting

everything else.

2. Edge detection

I. Smoothing

IV. Double thresholdingV. Edge tracking by hysteresis

The edge-pixels remaining after the non-

maximum suppression step are marked with their

strength pixel-by-pixel.

The edge detection algorithm uses double

thresholding.

2. Edge detection

I. Smoothing

Final edges are determined by suppressing all

edges that are not connected to a true (strong)

Thus strong edges will (almost) only be due to true

edges in the original image.

The weak edges can either be due to true edges

or noise/color variations.

3. Morphological operations

I. Morphological dilation

II. Morphological closing

Morphological operations are affecting the form, structure or shape of

an object.

used for getting a representation or description of the shape of

objects/regions.

II. Morphological closingDilation adds pixels to the boundaries of objects

in an image.

The number of pixels added to the objects in an

image depends on the size and shape of the

structuring element used to process the image.

II. Morphological closing Closing removes small holes(islands) and other

thin filaments of background pixels.

i.e.; it can fill holes in the regions while keeping

the initial region sizes.

4. Quality index

I. Error correction

II. Discard low quality images

Used in further modules as a parameter to check the quality.

QI has been designed so that it can be readily computed for every

input image once the QR code area has been extracted.

4. Quality index

I. Error correction

II. Discard low quality images

For error correction the Reed-Solomon code is

It is a non binary cyclic-error correcting code.

It is has some steps

Takes block of data.

Adds “redundant” bits to form a code

The code word can be decoded to

correct for lost or corrupted bits.

4. Quality index

I. Error correction

II. Discard low quality images The very low quality images are more complex

in the deblurring module, so the image will be

avoided

Contrast adjustment and code extraction: (a) captured image; (b) contrast- enhanced image;

(c) Binarized image; (d) external elements removal;(e) edge detection; (f) high edge density area

identification after dilation; (g) high edge density area identification after dilation and closing;

and (h) codeextraction

Pre-processing

Capturing image

QR code extraction

Pattern detection

Scanning the code

Although ideally QR codes are two-tone images.

Used some deblurring algorithms.

Pre-processing

Capturing image

QR code extraction

Pattern detection

Scanning the code

determine the proper compensating transform, the four

corners of the code have to be determined and the patterns

of the QR code detected.

The corner coordinates associated with the three finder

patterns are considered precise enough.

the fourth corner must be refined since it may be actually

defined by position of the finder patterns, which is

undetectable by the scanning, causing pixel loses.

Pre-processing

Capturing image

QR code extraction

Pattern detection

Scanning the code

Decoding the printed data to the digital data

Decoding QR code

The QR code to decoding

Decoding QR code cont…

Grouping to octets…

Decoding QR code cont…

Conclusion

In this paper, a practical image Pre-processing method was

proposed for QR code recognition.

The influence by background noise and inhomogeneous light and

geometric distortion was minimized.

In additional, it used alignment patterns to adaptively sample the

barcode in terms of regions, which greatly improved the recognition

The number of calculation of this algorithm is reduced, its

recognition speed is higher.

References

[1]IEEE Explore ,Munoz-mejias,Gonzalez-Diaz,Diaz-de-Maria:” A low

pre-processing system for restoring low quality QR code images”

[2]Wikipedia/QR-code

[3]Yunhua Gu,Weixiang Zhang,"QR code recognition Based on

Image Processing",International Conference on Information Science

and Technology ,March 26-28, 2011 Nanjing, Jiangsu, China

[4]M.Sun,LS.Fu,XTYang,S.H.zhang,"Image Analysis Method for QR

Code's Automatic Recognition",Journal of University of Electronic

Science and Technology of China, Vo1.38,No.6,pp.122-

125,Nov.2009.

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A low pre processing system for low quality QR code

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