(Đồ án hcmute) product classification using barcode

MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY FOR HIGH QUALITY TRAINING Ho Chi Minh City, Febuary, 2023 SKL 0 1 0 4 5 3 GRADUATION

MINISTRY OF EDUCATION AND TRAINING

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION

FACULTY FOR HIGH QUALITY TRAINING

Ho Chi Minh City, Febuary, 2023

SKL 0 1 0 4 5 3

GRADUATION THESIS MECHANTRONICS ENGINEERING TECHONOLOGY

PRODUCT CLASSIFICATION USING BARCODE

LE NGUYEN ANH KHOA BUI DINH NAM THANH ADVISOR: TRAN VI DO

STUDENT: TANG HOANG LONG

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION

FACULTY FOR HIGH QUALITY TRAINING

Ho Chi Minh City

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION

FACULTY FOR HIGH QUALITY TRAINING

Ho Chi Minh City

THE SOCIALIST REPUBLIC OF

GRADUATION PROJECT ASSIGNMENT

Student name: Tang Hoang Long Student ID: 18146045

Student name: Le Nguyen Anh Khoa Student ID: 18146034

Student name: Bui Dinh Nam Thanh Student ID: 18146059

Major: Mechatronics Engineering Class: 18146CLA

Advisor: Tran Vi Do Phone number:

Date of assignment: Date of submission:

1 Project title: PRODUCT CLASSIFICATION USING BARCODE

2 Initial materials provided by the advisor: _

3 Content of the project: _

4 Final product:

CHAIR OF THE PROGRAM

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ADVISOR

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THE SOCIALIST REPUBLIC OF

VIETNAM

Independence – Freedom– Happiness

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Ho Chi Minh City, Day Month…….,

Year… …

ADVISOR’S EVALUATION SHEET Student name: Tang Hoang Long Student ID: 18146045 Student name: Le Nguyen Anh Khoa Student ID: 18146034 Student name: Bui Dinh Nam Thanh Student ID: 18146059 Major: Mechatronics Engineering Project title: PRODUCT CLASSIFICATION USING BARCODE RO Technology Advisor: Mr Tran Vi Do EVALUATION 1 Content of the project:

2 Strengths:

3 Weaknesses:

4 Approval for oral defense? (Approved or denied)

5 Overall evaluation: (Excellent, Good, Fair, Poor)

6 Mark: … (in words: )

Ho Chi Minh City, Day , Month… , Year…

ADVISOR

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THE SOCIALIST REPUBLIC OF

VIETNAM

Independence – Freedom– Happiness

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Ho Chi Minh City, Day Month…….,

Year… …

PRE-DEFENSE EVALUATION SHEET Student name: Tang Hoang Long Student ID: 18146045 Student name: Le Nguyen Anh Khoa Student ID: 18146034 Student name: Bui Dinh Nam Thanh Student ID: 18146057 Major: Mechatronics Engineering Project title: PRODUCT CLASSIFICATION USING BARCODE EVALUATION 1 Content and workload of the project

2 Strengths:

3 Weaknesses:

4 Approval for oral defense? (Approved or denied)

5 Overall evaluation: (Excellent, Good, Fair, Poor)

6 Mark: … (in words: )

Ho Chi Minh City, Day , Month……., Year …

REVIEWER

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THE SOCIALIST REPUBLIC OF

VIETNAM

Independence – Freedom– Happiness

-

Ho Chi Minh City, Day Month…….,

Year… …

EVALUATION SHEET OF DEFENSE COMMITTEE MEMBER Student name: Tang Hoang Long Student ID: 18146045 Student name: Le Nguyen Anh Khoa Student ID: 18146034 Student name: Bui Dinh Nam Thanh Student ID: 18146059 Major: Mechatronics Engineering Project title: PRODUCT CLASSIFICATION USING BARCODE Name of Defense Committee Member:

EVALUATION 1 Content and workload of the project

2 Strengths:

3 Weaknesses:

4 Overall evaluation: (Excellent, Good, Fair, Poor)

5.Mark:……….(in words: )

Ho Chi Minh City, Day Month……., Year….…

COMMITTEE MEMBER

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Contents

FIGURES 1

ACKNOWLEDGMENT 3

ABSTRACT 4

CHAPTER 1 OVERVIEW OF THE TOPIC 5

1.1 Question 5

1.2 Object of the research 6

1.3 Research method 6

1.4 Research content 6

1.5 Research limit 7

CHAPTER 2 THEORETICAL BASIS 8

2.1 Overview of product categories 8

2.1.1 Methods of product classification 8

2.1.2 Product classification applied image processing 8

2.2 Overview of image processing 9

2.2.1 Concepts of image processing 10

2.2.2 Basic problems in image processing 11

2.2.3 OpenCV library and image processing methods used in the project 13

2.3 Overview of Barcode detection 18

2.3.2 Barcode types 20

2.3.3 Pyzbar Detection 24

2.4 Proposed Methodology 25

2.4.1 Preprocessing 26

2.4.2 Barcode decoding 26

2.4.3 Barcode displaying 27

2.5 Communication in industry 28

2.5.1 Communication Modbus 28

2.5.2 Profibus Communication 30

CHAPTER 3 DESIGN CALCULATIONS 32

3.1 Design requirements 32

3.2 System block diagram design 32

3.2.1 System block diagram 32

3.2.2 Working principle 33

3.3 Design each block 33

3.3.1 Central processing unit 34

3.3.2 Executive block 35

3.3.3 Signal block 43

3.3.4 Power block 44

3.4 Calculation of 3-axis X-Y-Z mechanism 45

3.5 Method of learning points in PLC 46

3.6 Design software 48

3.6.1 Algorithm Flowchart 49

3.6.2 Introduction to programming languages and software 51

3.7 System connection diagram 53

3.7.1 General circuit layout 53

3.7.2 Circuit layout to connect driver to PLC and motor 53

CHAPTER 4 RESULT 54

4.1 Results 54

4.1.1 Construction of mechanical parts 54

4.1.2 Electrical cabinet results 60

4.1.3 Display 61

4.2 Experiment 61

4.2.1 The object lies upright, in the middle of the conveyor belt 62

4.2.2 The object lies upright, to the right of the conveyor belt 62

4.2.3 The object lies diagonally, in the middle of the conveyor belt 63

4.2.4 The object lies diagonally, in the middle of the conveyor belt 63

4.3 Experiment result table 64

4.4 Review and evaluation 64

CHAPTER 5 CONCLUSION AND DEVELOPMENT 66

5.1 Conclusion 66

5.1.1 Pros 66

5.1.2 Cons 66

5.2 Development 67

REFERENCES 68

1

FIGURES

Figure 2 1 Classification of ripe tomatoes using image processing 9

Figure 2 2 Image processing technology 9

Figure 2 3 Description Pixel 10

Figure 2 4 Gray level range 11

Figure 2 5 Low and high resolution difference 11

Figure 2 6 Function image representation 12

Figure 2 7 OpenCV library logo 13

Figure 2 8 Gaussian filter with multiply size (5.5) and (11,11) 14

Figure 2 9 Illustrate dilation 15

Figure 2 10 Corrosion Magic Illustrated 16

Figure 2 11 Contour illustration 16

Figure 2 12 Approximation algorithm contour illustration 18

Figure 2 13 barcode component 19

Figure 2 14 GS1 barcode 21

Figure 2 15 NW-7 barcode 21

Figure 2 16 CODE39 barcode 22

Figure 2 17 CODE128 barcode 22

Figure 2 18 GS1 databar 22

Figure 2 19 Flowchart of Our Proposed Method 27

Figure 2 20 Communication in industry 28

Figure 2 21 Communication Modbus 29

Figure 3 1 System block diagram 32

Figure 3 2 Types of PLCs available today 34

Figure 3 3 Siemens PLC S7-1200 35

Figure 3 4 NEMA17 stepper motor 42x42x48 37

Figure 3 5 Stepper motor pinout 37

Figure 3 6 JGB37-3530 DC motor 39

2

Figure 3 7 Driver TB6600 40

Figure 3 8 Electromagnet 41

Figure 3 9 Relay Omron MY2N-GS DC24V 42

Figure 3 10 8 pins intermediate relay pin diagram 42

Figure 3 11 Camera HIKVISON DS-U02 43

Figure 3 12 Omron SS-5GL2 limit switch 43

Figure 3 13 24V honeycomb power 44

Figure 3 14 MCB CHINT NXB-63 45

Figure 3 15 learn points PLC 47

Figure 3 16 System Process Flowchart 48

Figure 3 17 Object identification flowchart 50

Figure 3 18 General circuit layout 53

Figure 3 19 Circuit layout to connect driver to PLC and motor 53

Figure 4 1 Complete system model from top to bottom (1) 55

Figure 4 2 Complete system model from top to bottom (2) 55

Figure 4 3 Complete system model from top to bottom 3D design 56

Figure 4 4 Object recognition structure 56

Figure 4 5 Conveyor structure (1) 57

Figure 4 6 Conveyor structure (2) 57

Figure 4 7 Conveyor structure 3D design 57

Figure 4 8 Object moving structure 58

Figure 4 9 Object moving structure3D design (1) 59

Figure 4 10 Object moving structure 3D design (2) 59

Figure 4 11 Complete electrical cabinet 60

Figure 4 12 Control interface 61

Figure 4 13 Case 1 result 62

Figure 4 14 Case 2 result 62

Figure 4 15 Case 3 result 63

Figure 4 16 Case 4 result 63

3

ACKNOWLEDGMENT

During the process of implementing the thesis topic, although facing many difficulties, but with timely help and support from the teachers and classmates, the thesis was completed on schedule The team would like to sincerely thank Mr Tran Vi

Do, the instructor, who enthusiastically guided and helped me throughout the implementation process as well as giving me advice, directions and solutions for the topic At the same time, the team would also like to thank the teachers in the Faculty

of High Quality for facilitating in terms of hardware equipment and software knowledge, providing the team with the basic and necessary knowledge to carry out the project as well as perfect the product

Thank you to the classmates of 18146CLA and the students in the thesis group for always being there and helping the group, going through the joys and difficulties during the learning process Everyone's sharing and contributions are always extremely valuable lessons so that the group can complete the project on time

Although the team itself has tried to complete the project report, in the construction process there are many drafting errors as well as limited knowledge, so there may be many shortcomings The group looks forward to receiving comments from teachers and students

Finally, the team would like to wish you good health, success and continue to train good students to contribute to the country Thank you sincerely

Students who did the project

LE NGUYEN ANH KHOA BUI DINH NAM THANH TANG HOANG LONG

ABSTRACT

The product classification system by barcode is a combination model of programmable control (PLC) combined with image processing capable of automatically detecting and classifying samples according to product barcodes The thesis consists of 2 parts: Building a mechanical model operating in 3 stable X-Y-Z axes, designing a system to automatically detect and process images on barcodes and position for mechanical classification models

After recognizing the barcode and the position of the object on the conveyor belt by the experimental image processing algorithm to achieve relative accuracy The lead screw mechanism and the electromagnet will receive a signal from the PLC to bring the product into the specified lane However, the system sometimes works is still unstable due to the influence of some factors such as natural light causing the shadow

of the object, causing the barcode recognition and the position to be not completely accurate

Finally, students use Visual Studio Code software to program image processing programs based on Python programming language on OpenCV open-source library that can communicate with PLCs As a result, a product classification system based on barcodes and communication with PLCs works in accordance with the requirements

CHAPTER 1 OVERVIEW OF THE TOPIC

1.1 Question

Up to now, the automation system and the role of automation have become very great in the construction and development of the country in general as well as in the development of enterprises in particular The application of technology to the factory's production lines as well as other operations of the factory helps to create closed, safe and quality implementation processes

In production sorting lines, for instance, shipping companies, it is essential to check the product’s name and its’ delivery location to ensure that the product is delivered to the right ordering person For example, classify the products in groups of districts; classify the products into their categories

For manual lines, workers use their eyes to check and find out the product’s location However, in a modern production line, it is required to produce a large quantity in a short time, and the requirements for accuracy are for product quality At that time, on the production line, the product must move at a very fast speed, moreover, there are very small products that the human eye cannot meet Therefore, it is extremely necessary to automate the inspection process instead of humans

A product sorting machine using barcode technology can be designed to increase efficiency and accuracy in sorting products Here are some potential benefits:

 Increased speed and efficiency: With a barcode sorting machine, products can be sorted much faster than manual sorting The machine can read the barcode and sort the product into the correct category automatically, saving time and reducing the likelihood of human error

 Improved accuracy: Barcode technology is highly accurate and can help ensure that products are sorted into the correct category every time This can reduce the risk of errors and save money by reducing the need for manual sorting and handling

 Better inventory management: By using barcode technology to sort products, the machine can also track inventory levels and provide real-time

data on stock levels This can help businesses to manage their inventory more efficiently, reducing waste and improving profitability

 Increased customer satisfaction: A product sorting machine can help businesses ensure that orders are fulfilled accurately and on time This can improve customer satisfaction and help businesses to retain their customers

1.2 Object of the research

The goal of the project is to design, program and operate the project "Design and construction of a product classification system" based on algorithms for detecting object barcodes, calculating the center of objects using Python language based on OpenCV open-source library

Use and program PLC proficiently, apply its functions to apply to the system Identify the barcode, the location of the product From there, it is possible to accurately classify products and put them in the lanes according to the original regulations

1.3 Research method

 Refer to the documentation on the home page http://www.siemens.com combined with direct practice on the Siemens 1214 CPU on the model

 Refer to some documents on related forums

 Survey some real models and some topics first

1.4 Research content

The topic covers the stages from design to successful building of the model, detailing the stages of model building from the process of understanding how the system works to mechanical design Provide a plan for selecting and connecting control and monitoring devices, system control algorithms The topic also gives comments, evaluates the achieved results and analyzes the limitations to provide solutions and develop the topic in the future The rest of the thesis has the following content:

Chapter 1: Overview of the topic: Presenting a preliminary overview of the

initial proposals of the topic, including the problem statement, the topic's objectives, the research content and the topic's limitations

Chapter 2: Theoretical basis: Presenting possible solutions for the design and

construction of the system

Chapter 3: Design calculations: From the design requirements and control

requirements of the topic, make design calculations and select system hardware devices

Chapter 4: System construction: Carrying out the construction and assembly

of the designed model; provide algorithm flowchart and control programming, system operation

Chapter 5: Results, comments and evaluation: Presenting the results of the

overview of the topic, what has been studied, comments and evaluation of the results

Chapter 6: Conclusions and directions for development: Presenting the

results achieved Evaluate the advantages, disadvantages and difficulties encountered during the implementation of the thesis Proposing ideas to improve shortcomings and future development orientations

1.5 Research limit

The topic focuses on researching and designing a stage in an industrial automatic production line After the product is completed, it will be classified and taken to the place where the next stages of the work are performed Currently, in factories and enterprises, there are many complete systems in both quality and aesthetics However, within the scope of a research topic, the topic is limited by:

 The system only classifies the trait as a barcode: The object of the classification is the product with the barcode and the size selected by the student

 In a single scan, each image processing algorithm barcode can only recognize one object

 Supervisory control capabilities are limited to a small range

 Average processing speed

CHAPTER 2 THEORETICAL BASIS

2.1 Overview of product categories

The product sorting system is an industrial solution that replaces humans to complete the product sorting process, from manual implementation to the use of automated systems to divide products according to each characteristic specified user

A complete system capable of sorting products with high reliability, continuous operation and minimal system downtime In addition, for jobs that require high concentration and continuous circulation, it is difficult for workers to ensure accuracy

in work That directly affects the product quality and the manufacturer's reputation Some of the currently applied product classification systems are: Product classification based on barcode - height - weight, product classification based on barcode scanners, product classification for image processing,

2.1.1 Methods of product classification

For systems using supporting devices such as color sensors, optical sensors, barcode readers, etc., there are advantages: easy programming, fast processing speed, easy operation, However, they also have some disadvantages: barcode scanning can

be more labor intensive, time is not high, it can only recognize a single product by barcode, barcode or height, but cannot receive power at the same time the above factors

2.1.2 Product classification applied image processing

The great advancement in image processing has opened a new step in product classification Applying image processing not only overcomes the shortcomings of the old product classification system, but also has high synchronization, it is possible to convert classified products according to the program In addition, it saves investment

costs because many properties can be integrated in a single processing program

Figure 2 1 Classification of ripe tomatoes using image processing

2.2 Overview of image processing

Image processing is a science and technology field, a new science compared to many other sciences, but its development speed is very fast This stimulates research and application centers, especially dedicated computers dedicated to it

This is a technique to enhance and process images received from devices such as cameras, webcams, Therefore, image processing has been used and strongly developed in important areas such as:

 Military: recognition and image processing in remote sensing, radar,

Figure 2 2 Image processing technology

satellite,

 Security and privacy: recognition of signatures, fingerprints, faces,

 Entertainment: video games, making actual games more entertaining

 Medical: X-ray images, biomedical, MRI,

2.2.1 Concepts of image processing

2.2.1.1 Digital photo

An image is defined as a two-dimensional function (F(x, y)), where x and y are the spatial coordinates, and the amplitude of F at any pair of coordinates (x, y) is called the intensity of that image at that point When the x, y and amplitude values of F are finite, we call it a digital image In other words, an image can be identified by a two-dimensional array specifically ordered by rows and columns A digital image consists

of a finite number of components, each of which has a specific position and value These components are called pixels

2.2.1.2 Pixel

The process of digitizing an image transforms from an analog value to a digital one and quantizes a value that in principle cannot distinguish two adjacent points with the naked eye In the past, this process uses the concept of Picture Element, which we often call Pixel – 12 image elements, pixels Thus, the image is a collection of pixels Each Pixel consists of a pair of x, y and color coordinates

A Pixel is usually represented by 1, 2, 8 or 24 bits of color

2.2.1.3 Gray level

Figure 2 3 Description Pixel

The gray level is the result of the corresponding transformation of a pixel's luminance value with a positive integer value Usually, it is specified in the range [0, 255] depending on the value that each pixel is represented Common gray level values are 16, 32, 64, 128, 256 (common because computer engineering uses 1 byte = 8 bits

to represent gray levels, gray levels use 1 byte to represent 28 = 256 values value from

0 to 255)

2.2.1.4 Image resolution

Image resolution is the pixel density assigned to a displayed digital image The distance between pixels must be chosen so that the human eye can still see the continuity of the image The selection of the appropriate distance creates a distribution density that is the image resolution and is distributed along the Ox (horizontal) and Oy (vertical) axes in two-dimensional space The higher the resolution of the image, the smoother and clearer the transformations in the image will be

Figure 2 5 Low and high resolution difference

2.2.2 Basic problems in image processing

2.2.2.1 Photo show

To represent images, people often use the feature elements of the image as pixels (pixels) Therefore, we can represent the image by a function of two variables containing the information Image representation models give a logical or quantitative

Figure 2 4 Gray level range

description of the properties of this function Image quality or the effectiveness of processing techniques depends on many factors such as image resolution, noise, etc

Figure 2 6 Function image representation

2.2.2.2 Image transformation

Transforming the representation of images under different viewing angles is convenient for image processing and analysis Commonly used methods: Fourier transform, Sine, Cosine

2.2.2.3 Photo analysis

In the image processing process, the extraction of features from the objects in the image is a very important step The properties of the object are extracted during processing depending on the recognition purpose, making the recognition more accurate, speeding up the computation time and reducing the storage space Some of the features include:

 Spatial features: probability distribution, gray level distribution, inflection point, image amplitude,

 Boundary - boundary feature: useful for analyzing invariant properties used to identify objects (angles, edges) To get the edge image we can use Gradient operator, Canny algorithm, Laplace operator

 Transform features: features of this type are extracted by performing area filtering Area filters called "feature masks" are mainly narrow slits with various shapes (circle, rectangle, square, ellipse)

2.2.3 OpenCV library and image processing methods used in the project 2.2.3.1 Open-Source library OpenCV

OpenCV is the leading open-source library in the fields of computer vision, image processing and machine learning It was released in 1999 under the BSD license (a group of permissive free software licenses), so it's completely free for both academic and commercial use Supports programming languages C++, C, Python, Java and Windows, Linux, MacOS, iOS and Android operating systems OpenCV is written in

C / C ++ language so it has very fast computation speed, focusing heavily on real-time applications Popular applications in image recognition and processing, automatic inspection and monitoring, Robots and autonomous vehicles, medical image analysis, virtual reality

Some outstanding features included in OpenCV library include: toolkit to support 2D and 3D image processing; Face Recognition; object recognition; gesture recognition; object movement and behavior recognition; human-computer interaction; control the Robot; virtual reality technology support

Figure 2 7 OpenCV library logo

2.2.3.2 The image processing methods used in the project

 Image smoothing

As with any other signal, images can also contain various types of noise, especially due to the source (camera sensor) Smoothing, also known as blurring, is one of the most common image processing operations used to reduce noise in images In this topic, students will choose a Gaussian filter to blur the input image

The Gaussian filter has the property that there is no excessive step function input in while minimizing rise and fall times In terms of image processing, any sharp edges in

the image are smoothed out while minimizing excessive blur It uses a Gaussian kernel with a user-selectable size, but the height and width of the kernel must be odd The filter converts all pixels in the image into a new set of pixels with the new value being the weighted average of the pixel's color values in the calculated kernel

Figure 2 8 Gaussian filter with multiply size (5.5) and (11,11)

 Dilation

Dilation is the convolution of images (or image areas), we call them A, with a multiplier we call them B The kernel can have many different shapes and sizes, but only there is a point called a single anchor point Usually, the kernel is a small square

or disc-shaped block with a central anchor point The kernel can be identified as a pattern or a mask, and what it means in the dilation algorithm is to locate the element with the maximum value When multiply B is scanned over the entire image, we calculate the maximum pixel value of the image area matched by the B kernel and replace the pixel specified by the anchor point with the calculated maximum value This causes the bright areas of the image to expand It is this expansion that is the origin of the term "expansion process"

The dilation on the binary image is described by the following mathematical expression:

A ⊕ B = {c | c = a + b, a ∈ A, b ∈ B} (2.1)

In which:

- A: The pixel matrix of the binary image.F

- B: Structure element

Image dilation will produce a set of pixels c belonging to D(i), we see that this

is a sum between A and B, A will be a subset of D(i)

Figure 2 9 Illustrate dilation

 Erosion

Erosion is the reverse process of expansion The behavior of the erosion operator

is equivalent to locating the minimum value of the luminous intensity on the region corresponding to the input convolution kernel Erosion creates a new image from the original image using the following algorithm: when multiplying B scans the entire image, we compute the pixel with the smallest value matching B and replace the image pixel value specified by the anchor point with the minimum value (for accuracy, the pixel on the output image will set the value equal to the minimum value of the pixels initialized by the multiplication on the input image)

Erosion is described by the following mathematical expression:

 Contour

Contour can be simply explained as a curve joining all the continuous points (along the boundary), having the same color or intensity Contours are a useful tool for shape analysis, object detection and recognition

 Moment

Image Moment is a set of statistical parameters to measure the position distribution

of pixels and their intensities Mathematically, the Image Moment M ij of the order (i, j) for a grayscale image with pixel intensity I(x, y) is calculated by the following formula:

To find the center, we calculate the average of the positions of all the points,

Figure 2 10 Corrosion Magic Illustrated

Figure 2 11 Contour illustration

with the formula:

 M10 and M01 are the values calculated in terms of x and y, respectively,

so that when performing the math, we can reduce the intensity of the measured pixels to be equal

If the distance from C to AB is less than ε, the point C will be removed from the Contour, otherwise if it is greater than or equal to ε, the point C will be kept and the Contour will be divided into 2 straight segments, AC and CB

For each segment AC and CB, the algorithm will be repeated in the same way as with the original AB

The Approximation algorithm is often used to determine the correct and clear shape of objects that can be of basic shapes such as quadrilaterals, triangles, even circles,

2.3 Overview of Barcode detection

2.3.1 Barcode

2.3.1.1 Barcode Overview

A barcode is “A machine- readable code in the form of figures and a pattern of resemblant lines of varying extents, published on and relating a product” A barcode can best be described as an “optic Morse code” Series of black bars and white spaces

of varying extents are published on markers to uniquely identify particulars The barcode makers are read with a scanner, which measures reflected light and interprets the code into figures and letters that are passed on to a computer Because there are numerous ways to arrange these bars and spaces, multitudinous symbolisms are possible But in verity a barcode is so much more Barcode systems help businesses and associations track products, prices, and stock levels for centralized operation in a computer software system allowing for inconceivable increases in productivity and effectiveness There are two types of barcodes direct – or 1D, and 2D Mostly recognizable barcode visually, the UPC (Universal Product Code) is a linear 1D barcode which consists of two parts the black and white pattern recognized as barcode

Figure 2 12 Approximation algorithm contour illustration

and the 12- digit UPC number The first six integers of the barcode represent the pattern as well as the manufacturer’s identification number The coming five integers represent the item’s number The last number is nominated as a check number which helps the scanner to determine if the barcode being scrutinized is free of error

A barcode’s density is determined by the “X ” dimension Density refers to the quantum of information that can be acquired in the bar code in a particular space, generally a direct inch While not intimately egregious, high density bar codes have low figures and low- density bar codes have high figures This is because individual characters correspond of some combination of bars and spaces that are each multiple

of “X ” When “X ” is small, the area needed for each character is lower than when “

X ” is large; therefore, the bar code can hold further per direct inch and is said to be of advanced density also, adding the range of the narrowest element (X) increases the space needed for each character and reduces the number of characters per inch Because the performing code is frequently relatively large, veritably low- density codes are frequently associated with operations similar as warehousing that bear reading bar codes from a significant distance (3 to 30 bases)

2.3.1.2 Barcode components

Figure 2 13 barcode component

①Quiet Zone (margin)

Quiet Zone is a blank margin located at either end of a barcode The

minimal margin between barcodes (distance from the outermost bar of one barcode to the outermost bar of another barcode) is 2.5 mm If the width of a Quiet Zone is insufficient, barcodes are hard for a scanner to read

②Start Character/Stop Character

The Start Character and the Stop Character are characters representing the start and the end of the data, respectively The characters differ depending on the barcode type

③Check Digit (Symbol check character)

The Check Digit is a digit for checking whether the encoded barcode data are correct

2.3.2 Barcode types

2.3.2.1 1D Barcode font Common product codes

Common product codes are broadly divided into two groups: UPC and EAN

・UPC = Universal Product Code

Common product code standardized in the U.S

・EAN = European Article Number

Common product code standardized in Europe based on UPC

In Japan, as a kind of EAN, JAN (Japanese Article Number) is used EAN (JAN) consists of 13 digits in the standard version and 8 digits in the shortened version UPC consists of 12 digits and 7 digits (including the check digit) in the standard and shortened versions, respectively EAN/UPC has been standardized as an ISO standard (ISO/IEC15420) and a JIS standard (JISX0507) In Japan, JAN codes are affixed to food, sundry articles, etc and are mainly used in Point Of Sales (POS) systems

The composition of a common product code is common in all countries, and a country code has been assigned to each country

For example, "49" and "45" are assigned to Japan

 GS1 Company Prefix

Code obtained by each manufacturer by application to the Distribution Systems Research Institute (GS1 Japan) via the Chamber of Commerce and Industry of each municipality/prefecture

Figure 2 14 GS1 barcode

 Product Item Code

Code determined by each manufacturer, similar to a product code

(e.g., container boxes) and for small labels affixed to noble metal and small products

It was standardized as a distribution product barcode symbol (JIS-X-0502) in 1987 and

as JIS (JISX0505) in 2004

CODE39

CODE39 enables the representation of alphabetical characters, and has been adopted as a military standard of the U.S Department of Defense (MIL-STD) It is widely used in the factory automation (FA) field and for AIAG, ODETTE and EIAJ tags Data that can be encoded are numerals (0 to 9), symbols ("-", " " (space), "$", "/",

"+", "%" and ".") and alphabetical letters (A to Z)

Figure 2 16 CODE39 barcode

CODE128

CODE128 can encode all 128 characters of ASCII It is used mainly in the factory automation (FA) and office automation (OA) fields Data that can be encoded are all

128 characters of ASCII It comprises three code sets

Figure 2 17 CODE128 barcode

GS1 Databar

GS1 Databar has three types of seven symbols GS1 Databar can represent data in

a smaller area than existing barcodes By using Application Identifiers (AIs), which are standardized by the GS1 Japan, information on product attributes, including expiration dates and lot numbers, can be represented In 2015, labeling GS1 Databar symbols on all ethical pharmaceuticals was made obligatory

Figure 2 18 GS1 databar

2.3.2.2 2D Barcode font PDF417

Large amounts of text and data can be stored securely and inexpensively when using the PDF417 barcode symbology The printed symbol consists of several linear rows of stacked codewords Each codeword represents 1 of 929 possible values from one of three different clusters A different cluster is chosen for each row, repeating after every three rows Because the codewords in each cluster are unique, the scanner is able

to determine what line each cluster is from

Figure 3.9: PDF417 barcode

Data Matrix

Data Matrix is a very efficient, two-dimensional (2D) barcode symbology that uses

a small area of square modules with a unique perimeter pattern, which helps the barcode scanner determine cell locations and decode the symbol Characters, numbers, text and actual bytes of data may be encoded, including Unicode characters and photos

Figure 3.10: Data Matrix

Maxicode

Maxicode is an international 2D (two-dimensional) barcode that is currently used

by UPS on shipping labels for world-wide addressing and package sortation MaxiCode

symbols are fixed in size and are made up of offset rows of hexagonal modules arranged around a unique finder pattern MaxiCode includes error correction, which enables the symbol to be decoded when it is slightly damaged

Figure 3.11: Maxicode

QR Code

QR-Code is a two-dimensional (2D) barcode type similar to Data Matrix or Aztec, which is capable of encoding large amounts of data QR means Quick Response, as the inventor intended the symbol to be quickly decoded The data encoded in a QR-Code may include alphabetic characters, text, numbers, double characters and URLs The symbology uses a small area of square modules with a unique perimeter pattern, which helps the barcode scanner determine cell locations to decode the symbol

Figure 3.12: QR Code

2.3.3 Pyzbar Detection

Read one-dimensional barcodes and QR codes from Python 2 and 3 using the zbar library

 Pure python

 Works with PIL / Pillow images, OpenCV / imageio / numpy ndarrays, and raw bytes

 Decodes locations of barcodes

 No dependencies, other than the zbar library itself

 Tested on Python 2.7, and Python 3.5 to 3.10 The older Zbar package is stuck in Python 2.x-land The Zbarlight package does not provide support for Windows and depends upon Pillow

2.3.3.1 ZBar versions

Development of the original Zbar stopped in 2012 Development was started again

in 2019 under a new project that has added some new features, including support for decoding barcode orientation At the time of writing this new project does not produce Windows DLLs The Zbar DLLs that are included with the Windows Python wheels are built from the original project and so do not include support for decoding barcode orientation If you see orientation=None then your system has an older release of Zbar that does not support orientation

2.3.3.2 Quality field

From zbar.h, the quality field is an unscaled, relative quantity: larger values are better than smaller values, where “large” and “small” are application dependent Expect the exact definition of this quantity to change as the metric is refined currently, only the ordered relationship between two values is defined and will remain stable in the future

In shopping system, it is observed that there is a laser scanner for detecting barcodes from a product But if there are multiple products, then the system needs more time to detect many products And it is tough to detect multiple product barcodes one

by one at a time So, our method is helpful for solving this issue so that it takes less time in shopping system to detect all the barcodes This proposed method is to detect multiple 1D and 2D barcodes from a snap EAN-13, Code 128 as 1D barcode and QR Code as 2D barcode were used For the detection of the barcodes, two steps were followed They are:

 To find the position of a barcode

 To decode the barcode

Once an image is obtained, the first step is to localize the barcode, or to find its location within the image Many methods to do this have been developed Again, after the barcode is localized using one of the methods in the previous section, it must be decoded in order to obtain the product’s information These processes were achieved

by the usage of Python language Firstly, an algorithm was processed to detect the barcode from the snap, then the job of decoding comes And the main benefit in this process is, detecting multiple barcodes at a time and detection of the barcode of cylindrical objects too attainable We have used OpenCV and Pyzbar library to detect and decode the barcodes from the snap There are three main methods of this proposed

barcode detection and decoding process They are: Preprocessing or Localizing,

Barcode decoding and Barcode displaying

2.4.1 Preprocessing

After taking an image as input, preprocessing is required this is just like resize the image, filtering the image Initially image had the resolution of 3146X3146 pixels which was reduced to 528X704 pixels The image is converted to grey scale image Then converted to Pyzbar compatible format to find image symbols

2.4.2 Barcode decoding

In this stage, the Pyzbar library decodes the barcode In the preprocessing stage image is already in ZBar compatible format Then scanlines are used to iterate over the symbols to extract type, data and location information

2.4.3 Barcode displaying

After decoding next step is displaying the symbols A quad is formed outside the barcode while displaying 2D barcode image If location is not a quad an outer boundary box is drawn

Figure 2 19 Flowchart of Our Proposed Method