BARCODE IDENTIFICATION BARCODE IDENTIFICATION BY USING WAVELET BASED BY USING WAVELET BASED

ENERGYENERGY

BARCODE IDENTIFICATION BARCODE IDENTIFICATION BY USING WAVELET BASED BY USING WAVELET BASED

ENERGYENERGY

Soundararajan Ezekiel, Gary Greenwood, David Pazzaglia

Computer Science Department

Indiana University of Pennsylvania

Indiana, PA, USA

Soundararajan Ezekiel, Gary Greenwood, David Pazzaglia

Computer Science Department

Indiana University of Pennsylvania

Indiana, PA, USA

ABSTRACTABSTRACTABSTRACTABSTRACT•we present a wavelet-based method for automatic barcode character detection.•Barcodes are widely used in a wide array of applications.• In order to facilitate barcodes, users must have a method for scanning a barcode. • The barcode scanner scans and identifies the characters present in the barcode. • Barcode scanners only work if the barcode image is recognizable. In the event of image distortion, it will fail to recognize the characters.Our method overcomes this problem, by reliably identifying the characters using multiresolution analysis. •This analysis, removes any existing noise by convoluting various filters. •We also, apply morphological operators to fill the gaps that are caused during the noise filtering process.

ContinueContinueOnce these gaps have been filled, we extract the

characters. Each character is then compared with a predefined

dictionary of characters by using two measures:• correlation,• multiresolution approximate coefficient energy

to find a matching character.• Finally, we display the best matched character. The

result suggests that this method is effectively capable of being applied to a broad range of barcodes.

• Since this method is simple, efficient, and has a real-time response, it can be implemented in embedded systems.

IntroductionIntroductionBarcodes were created as a replacement for

punch cards to identify product details. They have become very popular because of

their simplicity and accuracy in the business community

Barcode identification systems are also critical elements in today’s global industry

These systems optically read a merchandises identification code and transmit this to a computer.

The computer then extracts the merchandise details from its database using the identification code.

Introduction -- ContinueIntroduction -- ContinueBecause of the vastness of the business community, the identification standard consists of many different types of barcodes that have been developed out of necessity.

Barcodes have two classifications: one dimension and two dimension

These classifications differ both in the way they represent data as well as how they decode the data

Some decode the data as letters only, some as numbers only, some combine letters and numbers, and some decode special character strings of various length and type

Barcode scanners work well if the barcode being scanned is flat, not wider than the scanner, and the symbols are not distorted.

Introduction --- continueIntroduction --- continue However, sometimes the products do not follow the above

said conditions. For example, a roll of film or an old receipt does not follow

these conditions. If the barcode has become distorted and we cannot

visually identify the barcode characters, this will cause a problem.

This type of problem can be solved by implementing an image processing system that can efficiently identify the characters in the barcode.

we use a wavelet based method for automatic barcode character detection because it is simple, effective, and it can be implemented in embedded systems.

This method seems to be well suited for a wide variety of barcodes

DCT - Discrete Cosine TransformationDCT - Discrete Cosine Transformation– Encode

• Take image• Divide into 8x8 blocks• Apply 2-D DCT--- DCT

coefficients• Apply threshold value• Store the hidden message

in that place• Take inverse– store as

image

– Decode• Start with modified image• Apply DCT• Find coefficient less than

T• Extract bits• Combine bits and make

message

219 215 214 216 218 218 217 216

219 216 216 216 215 215 215 215 217 217 218 216 212 212 213 215 215 215 215 215 211 212 214 216 217 216 214 216 215 215 217 218 216 216 215 214 215 215 215 216 215 214 210 210 211 215 215 216

218 215 211 211 213 214 216 216

1720 1.524 7.683 1.234 1.625 0.9234 -0.07047 -1.055 5.667 3.475 -4.181 -1.524 1.152 1.637 1.016 0.38020.3711 -1.442 1.067 5.944 0.3943 -0.4591 0.1313 0.7812 3.888 -3.356 -1.97 3.265 0.5632 -0.939 -0.2434 0.2354 1.625 -2.279 0.4735 1.392 1.375 0.6552 -1.143 0.03459-4.049 -1.223 0.5466 -0.5425 -1.013 -0.2651 0.5696 -0.9296 1.876 1.924 -1.369 -1.132 -0.02802 -0.4646 0.1831 0.97290.8995 -0.7233 0.667 0.436 0.1325 -0.03665 -0.3141 -0.4749

Wavelets TransformationWavelets TransformationWavelets are basis function in continuous time.a basis is a set of linearly independent functions that can be used to produce all admissible functions f(t)

( )jkw t

,

( ) combination of basis functions ( )jk jkj k

f t b w t

The special feature of wavelet basis is that all functions ( )jkw t

are constructed from a single mother wavelet w(t). This wavelet is is a small wave ( a pulse). Normally it starts at time t=0 and end at time t=N Compressed = 0 (2 )j

jw w t Shifted k time = 0 ( ) ( )kw t w t k

Combine both we have ( ) (2 )jjkw t w t k

Haar Wavelet :- 1909 Haar, 1984– theory, 88– daubechies 89- Mallat 2-d, mra, -- 92- bi-orthogonal

Haar=

Message to be Hidden

Carrier Wavelet Wavelet

TransformationTransformationThresholdingThresholdingCompressionCompression

Stego image

Error ImageError Image

Inverse TransformationInverse TransformationExtract the Hidden MessageExtract the Hidden Message

figurefigure

Energy MeasureThe energy of an image is calculated by dividing the squared sum of all the approximate coefficients by the number of coefficients i.e.

12

0

1( ) [ ]

N

i

E iN

where E is the energy,

is the approximate coefficient vector, and N is the number of coefficients

Two-dimensional Correlation

The two dimensional correlation between two images Aand B of size

m n is computed by the following formula:

2 2

mn mnm n

mn mn

A A B Bc

A A B B

where A and Bare each a two-dimensional mean.

MethodologyMethodology Step1. start with an image that has to be analyzed.

If the image is a color image, convert it to grayscale or analyze red, green, and blue images individually.

Then a dictionary of characters is defined for character matching.

Further, we calculate energy measure for each character in the dictionary and store them for future comparisons in a vector E. The plot of the vector E is called the energy spectrum.

Step2. Next, the appropriate filter type and size is chosen to restore the image by removing the noise. This can be done by convoluting the image with the chosen filter.

ContinueContinue The convolution process may damage the

characters in the barcode image. Damages such as broken characters, thinning or

thickening of the edges, and the creation of gaps in the characters may occur.

Sometimes these conditions may be preexisting. To address the above said problems, we apply the

morphological operations such as: close, open, and fill .

These operations will enhance the image resolution and increase the quality of the characters.

ContinueContinueThen by extracting the characters from the

barcode, we calculate the energy measure and compare it with each element in E.

This comparison is done by using the thresholding technique , which gives a set of possible matching characters.

We then calculate the two dimensional correlation coefficients between the extracted character and each character in the matching set to ensure the best match.

Finally, we display the matched character.

predefined dictionary of characterspredefined dictionary of characters

ConclusionConclusion we have been able to extract characters and find their

matching characters successfully. Our experimental results have demonstrated that our

algorithm is effective for character restoration, extraction, and matching in an array of barcode images.

A foundation for wavelet-based method for automatic barcode character detection has been set forth.

The wavelet-based method can be applied to a variety of barcodes such as ISBN, Code 128, Code 39, and others.

However, further experimental analysis needs to be carried out for different barcode types, wavelet filters, noise removal masks, and mask sizes to adapt the wide array of existing character features.

More information – check out websitehttp://www.cosc.iup.eud/sezekielContact: sezekiel@iup.edu