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Abstract—In this report we present the method of

deciphering the barcode using image processing, and

its implementation on Matlab®. 1D barcode labeling

is a commercial method to store information about a

product for the checkout systems of large marts and

shopping centers. Nearly all products are labeled

with barcodes at the time of their manufacturing and

sometimes shop owners label products using their

own barcodes. The conventional bar code scanners

are not portable as they need both a power source

and a computer with the database of the product, to

be read. Also they are not readily available. It would

be convenient for the costumers to read and decipher

these barcodes using their portable devices such as

digital cameras and mobile phones, In order to gain

the specific information about the product provided

by the manufacturer. Our aim in this project was to

take those barcode images taken by cameras and

decipher them. We used edge detection to find the

edges of barcode and through the knowledge of the

standard that the barcode is encoded in; we convert

the image into the decimal code.

1. INTRODUCTION

Barcodes is a machine readable encoding process. It is

being used extensively throughout the world. There are

different standards of the barcode, each having its pros and

cons. The latest barcode standard is EAN, which was formerly

known as “European Article Number” but now is called

“International Article Number”. But we have chosen UPC,

Universal Product Code, because it is the most widely used

barcode and has an error detection mechanism in it, which aids

the deciphering process.

Barcodes are used to encode specific information about the

product, for the ease of consumers and shopkeepers. Several

types of devices are there to read these barcodes, e.g., LED

scanners, Laser scanners etc. These devices are cumbersome to

use and are neither readily available nor mobile. This limits the

product information to the shop owners only, to aid costumers

in extracting this information, portable solution to the

deciphering of barcodes must be provided. One of these

solutions is the deciphering of the barcode using handheld

devices such as mobile phones and digital cameras. These

devices take the picture of the barcode and then use either an

online platform to extract the information about the product or

use some other kind of connectivity to the database containing

the information about the product. Retrieval of such

information is vital for people with medical afflictions towards

some products and for the validation of price and legitimacy of

the product. Many efforts have been made to achieve the

deciphering using images of camera phone (see references for

details). These methods may be more accurate but they are

computationally expensive and time consuming. So we have

developed a method of robust deciphering [1] of the barcode

which is a practically viable solution. The details of our

method and its Matlab® implementation are discussed next.

2. OBJECTIVES

The main objectives of our project were:

Read a Barcode from a camera image

Decode the barcode

Remove any type of noise that may occur

Recognition & Conversion of Barcode using

Camera Image

Amna Meer Reg. No.442 Aneeba Masood Reg. No.443

Asadullah Hussain Reg. No.404 Rahma Shaukat Gill Reg. No.456

BEE-5D

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3. BARCODE DETECTION

The first step in the deciphering is the detection of barcode.

This can be easily achieved by several edge detection

techniques. We have employed the fact that barcode is an

intensity image. It contains vertical strips of black and white

lines. So we use the gray thresh value of the image to detect

the start and the end of the barcode strips. This can be

achieved by the wavelet transform method [3] or by

morphological operations [10] but we want a method that is

both quick and efficient so we use scanline based approach.

This method uses the fact that a horizontal scanline is at the

center of the barcode. If the image of barcode is not centered

or the center region contains distortions then the algorithm will

simply shift the scanline above or below the center of the

image until we get non distorted region. The Matlab®

implementation is given in Appendix A.

Fig. 1 The Scanline Approach [1]

4. BINARIZATION

The next step is the Binarization of the image. We have to

distinguish the barcode i.e., black strips from the white

background i.e., spaces. First the start and the end of the

barcode is detected i.e., first black strip and last black strip.

Then scanline scans the pixels from the center. Then the gray

thresh values are used to assign 1s to spaces and 0 s to strips.

If the gray thresh is other than that of black or white. We have

defined two ranges for gray thresh values, one range is allotted

1 for white and other is assigned a binary 0 for black. If the

gray thresh value of a pixel lies within the white range it is

assigned binary 1 i.e., we say that this pixel is white or space

but has been discolored, if it lies in other range then it is

assigned the opposite binary number i.e., 0 we say that the

pixel was black but has been decolored. In this way the

decolored pixels that may arise in the barcode image are

accounted for and thus a type of noise is taken care of. This

method is derived from the luminous method described in [1].

In this method without searching for the edges the scanline

binarizes all the pixels. The pixel selected first is the middle

one. To cater for the noises first the scanline pixels are

smoothed and then the luminance value Y(x) Ԑ [0, 1] is

calculated for each pixel, whose position is given by:

.

Then the local minima and maxima are computed. The

Matlab® implementation is described in Appendix A.

Fig. 2 Block Diagram of our Algorithm, Input is an image of barcode.

Which is detected and binatized by scanline approach, then error

check and decimanl conversion are applied.

5. ERROR CHECKING

Once the image has been binarized then all we have to do is to

use our knowledge about the construction of the barcode, this

in our case is achieved by the knowledge of UPC‟s encoding

process. In nearly all the Barcode standards we have a total of

13 decimal digits. The last is the parity bit. This digit is used

to provide the second step of the double error check.

A. The UPC standard [12]

An UPC-A barcode consists of four areas:

i. The number system

ii. The manufacturer code

iii. The product code

iv. The check digit

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Number System is for the class or type to which the product

belongs.

The manufacturer code is given to each manufacturer by UCC

council for companies.

The product code is a 5-digit number that the manufacturer

gives to his products.

The check digit verifies the validity of the product. It tells

whether the barcode has been scanned properly of not.

Fig.3 A standard UPC-A barcode [10]

The barcode begins & ends with a „101‟ or “black-white-

black” pattern, these are also termed as guard bars. Between

these guard bars, we have two blocks of black and white

strips, separated by a central bar which is a „01010‟ or “white-

black-white-black-white” pattern. The two blocks on either

side of the central bar contain the decimal code, which is

different for each product. Each decimal digit is encoded by

using 7 bits. Even and Odd alphabets are used for the

encoding. First digit is called the meta-number or induced

digit [1].

Fig. 4 A typical UPC barcode. Guard bars are highlighted green [14]

To ensure that the scanline does not go outside the boundary

of the image, we set our starting position in the middle of the

image, to the value where luminance is 0 or we start with a bar

(black). Then we scan left and right from this point by using

pre-determined steps or blocks of pixels. The choice of the

steps helps us in finding the guard bars, which are used for the

first step of the error detection.

The error detection is as follows:

If

The first 3 bits of the binarized image are equal to 101.

The bit 46 is 0, 47 is 1, 48 is 0, 49 is 1 and 50 is 0.

The last three bits i.e., 93, 94 and 95 are equal to 101.

Then

The binarized image is a valid UPC code. If any one of the

above mentioned conditions is not satisfied then the code is

not a valid UPC code.

The Matlab® implementation is given in Appendix A

6. CONVERSION

For the conversion of the binarized image to the decimal we

again use our knowledge of the UPC construction. There is a

lookup table [14] with which we compare our bits.

We ignore first and last 3 bits as they are the guard bits. Also

we do not consider the central bits. As these bits are same for

all the barcodes. Starting from the 4th

bit (first three bits are

guard bits) and selecting seven bits at a time. These seven bits

are compared with the lookup table of Figure 2 and by

comparison we allocate decimal values to the block of seven

bits. We can see from the table that if we are looking at bits to

the left of the central bits then we use the “Left Bit Pattern”

column to compare, while if we are looking at bits on the right

of the central bits we use the “Right Bit Pattern”.

Digit Left Bit Pattern Right Bit Pattern

0 0001101 1110010

1 0011001 1100110

2 0010011 1101100

3 0111101 1000010

4 0100011 1011100

5 0110001 1001110

6 0101111 1010000

7 0111011 1000100

8 0110111 1001000

9 0001011 1110100

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Once the 12 digits have been calculated the check sum bit is

calculated, it is calculated by the following method [14]

1. The digits that are in the odd numbered positions are

added and multiplied by 3.

2. The digits in the even numbered positions are added.

3. The sum of even numbered is added to the sum of

odd numbered.

4. The result is modulo 10.

5. If the result is zero then check bit is zero. Otherwise

subtract it from 10 and the answer is the result.

7. EXPERIMENTS

We used a database of images [15]. To test our project using

Matlab®. The Experiments were very assuring and we reached

a success rate of 97%. We also used images which were

affected by minor noise.

Fig. 5 (a) (b)

(d) (e)

Images (a) - (e) Successfully Deciphered

(f) Decoding Unsuccessful

The Matlab® implementation is given in Appendix A

8. CONCLUSION

Hence we are able to achieve all of our preset objectives for

this semester project, i.e., we have successfully deciphered

UPC barcode using image processing techniques in Matlab®.

We were also able to account for minor noise distortions as in

Figure 5 (d). For future our next step will be to remove as

many types of noises as we can from the image and add

support for other barcode standards such as EAN and Codabar

as well.

9. ACKNOWLEDGMENTS

We would like to thank Mr. Saad Qaisar and Ms. Jaweria

Amjad for their support throughout the semester.

1) Dr. Saad Qaisar

saad.qaisar@seecs.edu.pk

2) Jaweria Amjad

jaweria.amjad@seecs.edu.pk .

10. REFERENCES

[1]. Robust Recognition of 1-D Barcodes Using Camera

Phones by Steffen Wachenfeld, Sebastian Terlunen,

Xiaoyi Jiang, Computer Vision and Pattern Recognition

Group, Department of Computer Science, University of

M¨unster, Germany, 2006.

[2]. Mastering Matlab 7 by Duane Hanselman, Bruce

Littlefield

[3]. K. Wang, Y. Zou, H. Wang: 1D Bar Code Reading on

Camera Phones. Int. Journal of Image and Graphics,

7(3):529–550, 2007.

[4]. S. Wachenfeld, S. Terlunen, X. Jiang:

http://cvpr.uni-muenster.de/research/barcode

[5]. R. Muniz, L. Junco, A. Otero: A Robust Software Barcode

Reader Using the Hough Transform. In Proc. of the Int.

Conf. on Information Intelligence and Systems, 313–319,

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[6]. E. Ohbuchi, H. Hanaizumi, L. A. Hock: Barcode Readers

Using the Camera Device in Mobile Phones. In Proc. of

the Int. Conf. on Cyberworlds, 260–265, 2004.

[7]. A Bayesian Algorithm for Reading 1D Barcodes; Ender

Tekin, James Coughlan, The Smith-Kettlewell Eye

Research Institute

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[8]. Reading Challenging Barcodes with Cameras, Orazio

Gallo and Roberto Manduchi, University of California,

Santa Cruz

[9]. R. Adelmann, M. Langheinrich, C. Fl¨orkemeier:

Toolkit for Bar Code Recognition and Resolving

on Camera Phones – Jump Starting the Internet of

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[10]. D. Chai, F. Hock: Locating and Decoding EAN-13

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[11]. E. Joseph, T. Pavlidis: Bar Code Waveform Recognition

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[12]. http://www.morovia.com/education/symbology/upc-a.asp

[13]. http://www.morovia.com/education/utility/upc-ean.asp

[14]. en.wikipedia.org

[15]. http://www.google.com/imghp