Image Processing Bar Code Recognition

SHAYANI BATABYAL

ROLL NO:29

TY.BSC.IT

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Late Shri Vishnu Waman Thakur Charitable Trust’sBhaskar Waman Thakur College of Science,

Yashwant Keshav Patil College of Commerce,Vidya Dayanand Patil College of Arts.

Virar (W).

CERTIFICATE

This is to certify that case-study done on ”Image Processing Bar Code Recognition” By SHAYANI S. BATABYAL Seat no 29, in partial fulfillment of B.Sc IT degree.(SEM VI) examination had not been submitted for any other examination and does not form any other course undergone by the guide.

_____________ _____________ ______________

Subject Incharge Examiner Head of Dept.

Date: Date: Date:

COLLEGE SEAL:

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ACKNOWLEDEGEMENT

It is indeed a matter of great pleasure and proud privilege to be

able to present this project on “Bar Code Recognition”

I am thankful to our honourable principal Sir DR. R. Bhagat. We will express our deep regards to our Principal.

We are highly indebted to our project guide Prof. Pranali Thakare for her valuable guidance and we wish to record deep sense of gratitude and appreciation for giving form and substance to our project.

The completion of the project work is a milestone in the life of students and its execution is inevitable without the cooperation of project guide, professors, librarians and other classmates and seniors who provided with time to time valuable help and advice.

Lastly I would like to specially thank the department and non-teaching staff for their support and co-operation throughout the completion of the project

It is truly impossible to accredit and recall the debts of all the people who have directly or indirectly helped us in successful completion of project.

INDEX

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SERIAL NO.

TOPIC PAGE NO.

1 Introduction 42 Uses of Bar codes 53 Benefits of Bar code 64 Types of Bar codes 75 Symbology 86 Choosing an appropriate code for

simulation10

6.1 Code UPC-A7 Structure of UPC Number 127.1 Reason for choosing UPC-A code 138 MATLAB Simulation for Bar Code

scanner14

9 Working of Bar Code 1510 Image Processing Introduction 1610.1 Algorithm development in bar code

recognition17

10.2 Detecting and Rotating Slanted image

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10.3 Advance noise elimination techniques

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10.4 De-blur an image 2210.5 Original de-blur function 2411 Future enhancements to image

processing techniques25

12 Conclusion 2613 Bibliography and references 27

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INTRODUCTION

Bar Code Data

A barcode is a machine-readable representation of information (usually dark ink on a light background to create high and low reflectance which is converted to 1s and 0s). Originally, barcodes stored data in the widths and spacing of printed parallel lines, but today they also come in patterns of dots, concentric circles, and text codes hidden within images. Barcodes can be read by optical scanners called barcode readers or scanned from an image by special software. Barcodes are widely used to implement Auto ID Data Capture (AIDC) systems that improve the speed and accuracy of computer data entry.

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USES OF BAR CODES

Since their invention in the 20th century, barcodes have slowly become an essential part of modern civilization. Their use is widespread, and the technology behind barcodes is constantly improving. Some modern applications of barcodes include:

Practically every item purchased from a grocery store, department store, and mass merchandiser has a barcode on it. This greatly helps in keeping track of the large number of items in a store and also reduces instances of shoplifting (since shoplifters could no longer easily switch price tags from a lower-cost item to a higher-priced one). Since the adoption of barcodes, both consumers and retailers have benefited from the savings generated.

Document Management tools often allow for bar coded sheets to facilitate the separation and indexing of documents that have been imaged in batch scanning applications.

The tracking of item movement, including rental cars, airline luggage, nuclear waste, mail and parcels.

Recently, researchers have placed tiny barcodes on individual bees to track the insects' mating habits.

Many tickets now have barcodes that need to be validated before allowing the holder to enter sports arenas, cinemas, theatres, fairgrounds, transportation etc.

Used on automobiles, can be located on front or back.

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BENEFITS OF BAR CODES

In point-of-sale management, the use of barcodes can provide very detailed up-to-date information on key aspects of the business, enabling decisions to be made much more quickly and with more confidence. For example:

Fast-selling items can be identified quickly and automatically reordered to meet consumer demand,

Slow-selling items can be identified, preventing a build-up of unwanted stock,

The effects of repositioning a given product within a store can be monitored, allowing fast-moving more profitable items to occupy the best space,

Historical data can be used to predict seasonal fluctuations very accurately.

Items may be reprised on the shelf to reflect both sale prices and price increases.

Besides sales and inventory tracking, barcodes are very useful in shipping/receiving/tracking.

When a manufacturer packs a box with any given item, a Unique Indentifying Number (UID) can be assigned to the box.

A relational database can be created to relate the UID to relevant information about the box; such as order number, items packed, qty packed, final destination, etc…

The information can be transmitted through a communication system such as Electronic Data Interchange (EDI) so the retailer has the information about a shipment before it arrives.

Tracking results when shipments are sent to a Distribution Center (DC) before being forwarded to the final destination.

When the shipment gets to the final destination, the UID gets scanned, and the store knows where the order came from, what's inside the box, and how much to pay the manufacturer.

The reason bar codes are business friendly is that bar code scanners are relatively low cost and extremely accurate - only about 1/100,000 entries will be wrong.

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TYPES OF BAR CODES RECODERS

There are currently four different types of bar code readers available. Each uses a slightly different technology for reading and decoding a bar code.

Pen Type Readers and Laser Scanners:

Pen type readers consist of a light source and a photo diode that are placed next to each other in the tip of a pen or wand. To read a bar code, you drag the tip of the pen across all the bars in a steady even motion. The photo diode measures the intensity of the light reflected back from the light source and generates a waveform that is used to measure the widths of the bars and spaces in the bar code. Dark bars in the bar code absorb light and white spaces reflect light so that the voltage waveform generated by the photo diode is an exact duplicate of the bar and space pattern in the bar code. Laser scanners work the same way as pen type readers except that they use a laser beam as the light source and typically employ either a reciprocating mirror or a rotating prism to scan the laser beam back and forth across the bar code.

CCD Readers:

CCD (Charge Coupled Device) readers use an array of hundreds of tiny light sensors lined up in a row in the head of the reader. Each sensor can be thought of as a single photo diode that measures the intensity of the light immediately in front of it. Each individual light sensor in the CCD reader is extremely small and because there are hundreds of sensors lined up in a row, a voltage pattern identical to the pattern in a bar code is generated in the reader by sequentially measuring the voltages across each sensor in the row.

Camera-Based Readers:

The fourth and newest type of bar code reader currently available are camera-based readers that use a small video camera to capture an image of a bar code. The reader then uses sophisticated digital image processing techniques to decode the bar code. Video cameras use the same CCD technology as in a CCD bar code reader except that instead of having a single row of sensors, a video camera has hundreds of rows of sensors arranged in a two dimensional array so that they can generate an image.

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SYMBOLOGY

The mapping between messages and barcodes is called a symbology. The specification of a symbology includes the encoding of the single digits/characters of the message as well as the start and stop markers into bars and space, the size of the quiet zone required to be before and after the barcode as well as the computation of a checksum.

Linear symbologies can be classified mainly by two properties:

Continuous vs. discrete: Characters in continuous symbologies usually abut, with one character ending with a space and the next beginning with a bar, or vice versa. Characters in discrete symbologies begin and end with bars; the intercharacter space is ignored, as long as it is not wide enough to look like the code ends.

Two-width vs. many-width: Bars and spaces in two-width symbologies are wide or narrow; how wide a wide bar is exactly has no significance as long as the symbology requirements for wide bars are adhered to (usually two to three times more wide than a narrow bar). Bars and spaces in many-width symbologies are all multiples of a basic width called the module; most such codes use four widths of 1, 2, 3 and 4 modules.

Some symbologies use interleaving. The first character is encoded using black bars of varying width. The second character is then encoded, by varying the width of the white spaces between these bars. Thus characters are encoded in pairs over the same section of the barcode.

The different bar code symbologies support different types and amounts of data therefore you normally choose a particular symbology based on the type and amount of data that you want to encode in your bar codes.

Symbology Data Capacity

UPC-A 12 numeric digits - 11 user specified and 1 check digit.

UPC-E 7 numeric digits - 6 user specified and 1 check digit.

EAN-8 8 numeric digits - 7 user specified and 1 check digit.

EAN-13 13 numeric digits - 12 user specified and 1 check digit.

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Code 39 Code 93 Code 128 EAN-UCC 128

Variable length alphanumeric data - the practical upper limit is dependent on the scanner and is typically between 20 and 40 characters. Code 128 is more efficient at encoding data than Code 39 or Code 93. Code 128 is the best choice for most general bar code applications. Code 39 and Code 128 are both very widely used while Code 93 is rarely used.

I 2 of 5 Variable length numeric data - the practical upper limit is dependent on the scanner and is typically between 20 and 50 characters.

Data Matrix Data can consist of any type of data including binary or alphanumeric and be up to 3116 bytes in length.

Aztec Data can consist of any type of data including binary or alphanumeric and be up to 3750 bytes in length.

Maxicode Maxicode can hold up to 93 alphanumeric characters or 138 numeric digits. Maxicode is used almost exclusively for United Parcel Service package identification.

PDF417 PDF417 is a little more complex and it is difficult to say exactly what its capacity is because it depends greatly on the type of data that you encode in a PDF417 symbol as well as the amount of error correction capacity that you choose to use in a PDF417 symbol.

For general binary data with no error correction enabled, a single PDF417 symbol can hold up to 1108 bytes. If the data consists of all numeric digits, then a single PDF417 symbol can hold up to 2725 digits. If the data consists of alphanumeric data, you can encode a maximum of 1850 bytes. If you have a mix of alphanumeric and binary data, the capacity will be somewhere between 1108 and 1850 bytes and will depend on the content of the data.

All of our bar code software products use an extremely efficient encoding algorithm that will squeeze the maximum number of bytes possible into a PDF417 symbol however it still must work within the limits of the symbology specification.

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CHOOSING AN APPROPRIATE CODE FOR SIMULATION

Code UPC - A For the purpose of sample testing, we will consider a barcode of the UPC-

A format. The reason for this , is that the size of a UPC-A barcode is always the

same - 95 bits. The UPC-A code consists of 12 digits, and each digit is represented by a

series of black and white bars. The corresponding digits are deciphered as :

o UPC-A barcode digits are coded such that the left 6 digits and the right 6 digits are separated by a middle guard of 0-1-0-1-0, that is, space-bar-space-bar-space.

o The left hand side codes have 10 possible space-bar combinations, with odd parity. It is evident, that the left hand codes start with a space. The bit patterns and widths are as follows:

Left Hand Side Codes:

0: 0001101 3-2-1-1

1: 0011001 2-2-2-1

2: 0010011 2-1-2-2

3: 0111101 1-4-1-1

4: 0100011 1-1-3-2

5: 0110001 1-2-3-1

6: 0101111 1-1-1-4

7: 0111011 1-3-1-2

8: 0110111 1-2-1-3

9: 0001011 3-1-1-2

On the other hand, the right hand side bit patterns relating to each digit are essentially ones complements of the left hand side pattern. They have an even parity and start with a bar. The bit patterns are as follows:

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Right Hand Side Codes (Remember these are the ones complement!):

0: 1110010

1: 1100110

2: 1101100

3: 1000010

4: 1011100

5: 1001110

6: 1010000

7: 1000100

8: 1001000

9: 1110100

One thing to note is that at the start and end of each barcode, there is a particular bit pattern, 101, indicating to the deciphering program where the barcode is initialised and concluded.

Barcodes, as stated above, due to the differing widths of the bars and spaces can essentially be composed of binary code, i.e. 1s or 0s. Thinking about the notion of barcode recognition within MATLAB is probably much simpler as we need to determine a way to convert the barcode image of bars and spaces into perhaps a graph, with the vertical axis ranging from 0 to 1, corresponding to the bars and spaces, etc. To convert the image to a graph as such would then allow us to manipulate MATLAB to read the graph and determine the numbers involved.

The UPC-A barcode is the most common and well-known symbology in the United States. You can find it on virtually every consumer goods in your local supermarket, as well as books, magazines, and newspapers. There are a number of UPC variants, such as UPC-E, UPC 2-digit Supplement, UPC 5-digit supplement.

UPC-A encodes 11 digits of numeric data along with a trailing check digit, for a total of 12 digits of barcode data.

0, 1, 2, 3, 4, 5, 6, 7, 8, 9

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STURCTURE OF UPC NUMBER

An UPC-A number consists of four areas: (1) The number System; (2) The manufacturer code; (3) the product code; (4)The check digit. Normally the number system digit is printed to the left of the barcode, and the check digit to the right. . The manufacturer and product codes are printed just below the barcode, separated by the guard bar.

NS Description

0 Regular UPC code

1 Reserved

2 Weight Items

3 Drug/Health Items

4 In-store use on non-food items

5 Coupons

6 Reserved

7 Regular UPC code

8 Reserved

9 Reserved

1. Number System : The number system is the first digit in the UPC number to identify the type of the product. For example, if the barcode starts with digit 5, this barcode is a coupon code.

2. Manufacturer Code : The manufacturer code is assigned by UCC council to each manufacturer or company which distributed goods that uses UPC-A barcode. Note that UCC has started to assign manufacturer code longer than 5 digits to conserve the numbering resource.

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3. Product Code: The product code is assigned by the manufacturer. The product code is a 5-digit number so it can accommodate 99,999 possible product codes for each manufacturer. That is far enough for any manufacturer in the world!

4. Check Digit: The check digit is used to verify that the barcode is generated or scanned correctly. The check digit is calculated based on the rest of the barcode digits. Read the following section to learn how to calculate the check digit.

Reason for choosing the UPC-A code

The main purpose of selection of this code is that the length of the barcode is always a standard 95 bits long. This length does not vary depending on its usage, or the number of products that it represents. Initially, Code 39 was chosen as a standard to run the simulation in, but the length of a code 39 Barcode varies greatly depending on the product for which it is made. Hence, using a standard 95 bit format makes it easier to implement the Image processing algorithms as the image cropping and barcode reading algorithms become easier to define.

MATLAB SIMULATION OF BARCODE SCANNER

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Simulating a Barcode Reader using MATLAB

The benefits are:

To provide the user with an interface when he can input any scanned image containing a barcode

To correctly scan the barcode segment from the scanned input image

Decode the barcode segment

Present the user with the decoded output

In order to ensure that the barcode segment is read as accurately as possible, we need to perform the following operations on the scanned image once it has been input by the user:

Image rotation (the barcode is rotated with respect to the camera)

Noise (poor signal to noise ratio, bad lighting conditions, image taken through glass, and so on)

Blurriness of the image (the camera is out of focus)

Now, Barcode Recognition involves a wide range of activities to ensure that the give image is properly processed and deciphered by the program. This project aims to correctly decode as many images as possible, though it may not be possible to accurately decipher each image.

Hence, the order followed to process a scanned image will be :

1) Create a GUI for the user

2) Clean the image by debluring it or by removing noise (if required)

3) Angular rotation of the image in case it isn't properly aligned

4) Barcode Image recognition

5) Barcode Decoding

6) Display the output.

WORKING OF A BAR CODE SCANNER

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Basically there are three functional parts to the bar code scanner itself:

1. Illumination system2. Sensor/Converter3. Decoder

Barcode scanners begin by illuminating the code with the red light. The sensor of the bar code scanner detects the reflected light from

the illumination system and generates an analog signal with varying voltage that represents the intensity of the reflection.

The converter changes the analog signal to the digital signal which is fed to the decoder.

The decoder interprets the digital signal, does that math required to confirm and validate that the bar code is decipherable, converts it into the ASCII text , formats the text and send it to the computer

the scanner is attached to.

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IMAGE PROCESSING INTRODUCION

The challenge in this case study is to be able to detect a barcode on an image and we have to account for the following situations: blurriness, slanted barcodes, light intensity of images, noise in images, more than one barcode in the image and upside down barcodes.

Algorithm Development in Matlab for Barcode Recognition

The following flow chart outlines the image processing algorithm that our group has used in order to recognise a barcode in an image:

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Detecting and Rotating Slanted Image

This barcode detection algorithm can also detect if the barcode is slanted and readjust the barcode so that it is straight. Actually very simple maths is behind this part of the algorithm! Using the Bounding Box property of Regionprops this will output the upper-left x and y coordinates, the width and the height of each area found in the image (i.e. the bars in the barcode). From this if we take the x and y values of the first and last bar and calculate their gradient, if this is not zero then the barcode must be slanted. Since we have the gradient we can then calculate the degree to rotate the image. The only major disadvantage is that it will work for barcodes that are rotated -90 to 90 degrees, if the barcode is rotated more than this the resultant barcode will be upside down (i.e. the barcode will still be straightened but it will be upside down). Below shows this part of the algorithm in action!

Figure : Rotate Barcode Function

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Advanced Noise Elimination Techniques

The basic algorithm will go through three elimination stages:

1. Clear any group/s of pixels that are touching the edge/border of the image

2. Clear any group/s of pixels less than 100 pixels^2 in area for image sizes > [300 300] or 50 pixels^2 for those smaller.

3. Clear any groups of pixels that are not straight (since eccentricity of a straight line is 1).

What happens when there is noise in the image, for example letters such as I, L, T etc that make it through the first three elimination stages? This will cause the resulting output image to include other noise not just the barcode. Hence we need to use more of the Region Properties to eliminate this noise. There are several options to the do this.

1. Firstly you can use the property Centroids that outputs the x and y values of the centre of each area in the image. From this you can then calculate the distance between each centroid of a group of pixels in an image. The barcode will have a small distance between each bar of the graph.

The advantage here is that if a bit of noise was on the same x axis as the barcode so possibly make it through the above elimination technique, the distance would be too far away from the barcode, hence it would be eliminated.

Disadvantage of this technique is that if there is noise scattered around the barcode it will calculate the distances from bars to the noise and thus bars will be eliminated.

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Fig. Distance Noise Elimination Function

Fig. Distance Noise Elimination Function Output

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2. This option uses the property area, so it will output the area of each of the groups of pixels left in the image both barcode and noise. Generally by this stage there are just small groups of noise left that weren't eliminated by the 100 threshold or maybe a really large group. Advantage of this option is that it does not matter if noise has made it through to this stage or not and it is easy to calculate ( a lot more efficient than calculating the distance!). Disadvantage if the area of the noise is the same size as the barcode it will still make it through!!!

Fig. Area Noise Elimination Function

3. Lastly you can use the property Centroids again this time though we will be doing something different with the x and y values. A barcode would generally be in the same region of the image, so you can use the median and standard deviation calculations to determine the region of the barcode (assuming that if there is any noise it will mainly be random outliers in the image.) Advantage of this technique is that it can identify the region of the image where the barcode is and eliminate any outlier noise. Disadvantage is that if all the noise is eliminated in the first three stages and if the barcode takes up the whole image you may lose bars of the barcode!!! Second disadvantage is if the barcode is on a 45 degree angle may lose outer bars of the barcode. Both are not good!!

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De-blur an Image

One of the requirements we would like our program to have is the capability of being able to de-blur an image and recognise if there is a barcode on the image and if possible read that barcode. To de-blur an image is quite difficult and it all depends on how badly "blurred" the image is in the first place and it can actually be modelled by the following equation (based on help in Image Processing toolbox):

o g = H*f + n o g = blurred image o H = distortion operator, also called Point Spread Function (PSF) o f = original image o n = additive noise.

Now the PSF is important as it describes the degree to which an optical system blurs a point of light. In mathematical terms the PSF is the inverse Fourier Transform of the Optical Transfer function (OTF). The distortion operation when convolved with the image, creates the distortion.

The problem with our blurred images will be that we do not know the distortion operator, i.e. if the picture is blurred to begin with we don't know the exact PSF of it. This is why we have implemented the blind deconvolution algorithm because it will perform the deblurring without knowledge of the original PSF.

This function is also quite an exhaustive function so it is recommended to have pictures of pixel size less than 1000 by 1000.

Below is example of how our original deblur algorithm worked. So we have our original image:

Figure : Blurred image

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We apply the de-blur function that does five steps (this algorithm is based on the help file 'De-blurring Images Using the Blind De-convolution Algorithm)

1. Uses an initial PSF based on a Gaussian distribution, now this was chosen because it best represents distortion of a camera lens being out of focus

2. The first iteration will use an array 4 pixels smaller than the PSF, i.e. UNDERPSF

3. The second iteration will use an array 4 pixels bigger than the PSF, i.e. OVERPSF

4. The third iteration will use a PSF of the same size, INITPSF

5. By using the edge function and changing threshold values we can get an array of "Weighted" values that will help with the final iteration of the blind de-convolution function .

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Original De-blur Function

This is quite an exhaustive function and to be practical in terms of performance time it was decided to only use the first iteration of UNDERPSF.

Furthermore we found that with images that were only slightly blurred putting them through the above five iterations would make the image worse than before and generally the UNDERPSF did a pretty good job of de-blurring these slightly blurred images.

Hence we use only the one iteration of the blind de-convolution and as you can see below we were able to successfully de-blur several images and thus read the barcodes.

It must be noted that the blurring that occurs in the first place is RANDOM hence this is a very basic de-blur function that will only work on images that happen to be blurred a special way.

Figure : Image to be de-blurred

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FUTURE ENHANCEMENTS TO IMAGE PROCESSING TECHNIQUES

1. We would like to improve the functionality of the de-blur function so make the user able to input different variables so that other blurred images can be read. At the moment the random Gaussian PSF has fixed variables. This enhanced functionality would require user input fields in the GUI that would then be inputted into the de-blur function. The user would also need to understand the types of variables that they should enter in so for this enhancement there would need to be a help page on suitable types of input variables.

2. We would like to improve the Graythresh technique of converting the image from a uint8 to a binary (black and white) image. In the design process it details about the troubles with light intensity in photos if there is a lot of light some of the black parts in the image will be converted to white instead of black! A way to enhance this would be to look at a histogram of the light intensity of the images and if there is not a clear enough distinction between white and black do an image enhancement function that would attempt to lessen the light intensity in the image. This would require another checkbox function that the user could tick if they had an image that has been distorted by light intensity.

3. We would like to be able to identify two or more barcodes in an image as well. At the moment if there are two barcodes and you tick noise elimination one barcode will be outputted but the function doesn't know that there is another barcode in the image. This would have to do with the region properties function again and seeing the region of the barcodes as two distinct barcodes so output two arrays. This functionality would require changes to the GUI to be able to process two different outputs for different barcodes.

4. We would like to be able to identify 2D barcodes, these are not just made up of bars so our original find barcode function would not work. We would have to come up with another function altogether for finding 2D barcodes in an image.

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CONCLUSION

Bar code technology is very useful technology. It is very cheap. Highly useful for identifying objects.

In this case study, we presented an image processing framework for recognition of ID bar codes.

This project aims at simulating a camera based barcode scanner. As already described above, the camera based scanner captures the entire image of a product and then uses advanced image processing techniques to decode it.

We process the image (if the user wishes) and perform image cleaning processes of noise removal on it. The de-blurring algorithm is used for the purpose of image modification and image processing to make the image machine readable.

The image reading algorithm performs image cropping and image resizing processes on the image to convert the machine readable image into a 95 bit array that makes it very simple to decode according the barcode decoding program.

One can use any simple scanner and scan the barcode segment of the image and then process it. The output decoded barcode can be then linked to any database management software, and in this way, the monitoring of issuance of books in a library can be obtained. Similarly, applications where the speed of scanning and the magnitude of images are not large, and where the products do not need to be scanned at an extremely rapid rate, can make use of this software for carrying out daily activities.

The UPC-A code was chosen only due to the convenience that it has only a standard length of 95 bits.

The barcode recognition algorithm can enable the user to use the same program to read barcodes of other formats.

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BIBLIOGRAPHY AND REFERNCES:

http://www.ukessays.com/essays/information-systems/ bar-code-data.php

www.google.com

“Barcode.” Wikipedia, The Free Encyclopedia. Wikimedia Foundation, Inc. 8. Dec. 2011. Web. 9. Dec.2011.

“Universal Product Code.” Wikipedia, The Free Encyclopedia. Wikimedia Foundation, Inc. 2 Dec. 2011. Web. 2 Dec. 2011