Smart-Bill Automation System

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072
© 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 554
Smart-Bill Automation System
Prof. Nilaja Deshmukh1, Sejal Kathane2, Gayatri Suryawanshi3, Shreya Mahatme4,Parikshit Gaikwad5
1Professor, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India
2Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India
4Student , Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India
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Abstract - The ever-evolving retail sector demands
innovative solutions to streamline and enhance the shopping
experience for both consumers and retailers. This paper
introduces an AI-based auto billing system meticulously
designed to address the inherent complexities of the
traditional retail checkout process, especially concerning
items like fruits and vegetables that defyconventionaltagging
mechanisms such as RFID tags and bar-codes. Recognizing the
distinct challenges posed by perishable goods, the proposed
system integrates advanced technologies, notably computer
vision and deep learningtechniques, toautomatetheweighing
and billing processes seamlessly. Central to this innovative
solution is the utilization of Raspberry Pi 3, a microprocessor
equipped with a specialized camera module and a load cell.
The system's operational framework encompasses capturing
high-resolution images of individual fruits and vegetables,
leveraging deep learning techniques such as ImageNet built
upon Convolutional Neural Network (CNN) architectures.
Further enhancing the classification efficacy, machine
learning techniques, including K means clustering, are
employed to categorize products into their respective groups,
ensuring precise billing calculations. Incorporating a
multifaceted approach, the camera module facilitates real-
time image capturing of items positionedonadesignatedtray,
strategically integrated with a load cell for accurate weight
measurements. Concurrently, pre-defined pricing metrics for
various items per kilogram are inputted into the Raspberry Pi
microprocessor. Leveraging the computationalprowessof the
Python programming language, the system orchestrates
intricate algorithms to compute the cumulative cost of
selected items, subsequently displayingthetotalamounton an
integrated monitor. Emphasizing paramount aspects such as
efficiency, accuracy, and safety, this AI-based autobilling
system transcends conventional retail paradigms, offering a
transformative solution that harmonizes technological
innovation with pragmatic retail requirements. By seamlessly
amalgamating cutting-edge technologies, this pioneering
system not only elevates the retail experience but also
underscores the potential of AI-driven solutions in redefining
modern retail landscapes.
Key Words: Billing System, Checkout Mechanism, Retail
Technology, Artificial Intelligence(AI) in Retail,
Computer Vision, DeepLearninginRetail,Convolutional
Neural Networks (CNN),Weight Sensing Technology,
Raspberry Pi in Retail, Automated Checkout.
1.INTRODUCTION
The rapid trajectory oftechnological advancementsinrecent
decades has fundamentally reshaped the fabric of our daily
lives, ushering in an era characterized by unparalleled
convenience, efficiency, and innovation. The contemporary
technological landscape is punctuated by an eclecticarray of
transformative technologies, encompassing artificial
intelligence (AI), blockchain, cloud computing, the Internet
of Things (IoT), data mining, augmented reality (AR), and
virtual reality (VR), among others.Eachoftheseinnovations,
in its unique capacity, has catalyzed paradigm shifts across
diverse sectors, redefining operational frameworks,
enhancing user experiences, and fostering unprecedented
possibilities. Central to this mosaic of technological
innovations is the realm of artificial intelligence, an
expansive domain that encapsulates machine learning and
deep learning paradigms. Within this ambit, deeplearning,a
subset of machine learning, has emerged as a cornerstone,
revolutionizing intricate processes and catalyzing
advancements previously deemed implausible.Theintrinsic
architecture of deep neural networks, reminiscent of the
intricate operations of the human brain, facilitates
unparalleled capabilities in pattern recognition, decision-
making, speech recognition, and myriad other applications.
The omnipresence of neural networks is palpable,
underpinning transformative applications ranging from
ubiquitous virtual assistants like Google's Assistant and
Apple's Siri to intricate financial services, encompassing
fraud detection, risk assessment, and market research
endeavors. In alignment with this transformative trajectory,
the crux of this project endeavors to harness the latent
potential of deep learning algorithms, specifically
Convolutional Neural Networks(CNN),andmachinelearning
techniques, exemplified by K-means clustering. The
overarching objective converges on designing an innovative
system poised to revolutionize the retail landscape,
particularly the billing process. By adeptly leveraging deep
learning algorithms, the system aspires to seamlessly
identify an expansive array of edible fruits and vegetables,
transcending manual interventions and encapsulating
efficiency. Furthermore, augmenting its capabilities, the
integration of load cell technology facilitates automated
weight measurements, culminating in precise cost
estimations and streamlined billing processes. In essence,
this project epitomizes the symbiotic amalgamation of

cutting-edge technologies and pragmatic applications,
underscoring a commitment to fostering innovation,
enhancing operational efficiencies, and redefining
conventional paradigms within the retail sector. Through a
meticulous explorationofdeeplearningalgorithmsandtheir
transformative potential, this research elucidates a
pioneering blueprint poised to reshape contemporary retail
dynamics, thereby heralding a new epoch characterized by
efficiency, accuracy, and unparalleled convenience.
2. BACKGROUND AND LITERATURE REVIEW
2.1 Traditional Checkout Challenges
The conventional checkout mechanism, emblematic of
manual item scanning and human-operated processes, has
long been a staple in retail environments. However, this
traditional paradigm is not without its inherent challenges
and limitations.
Firstly, the manual scanning of items introduces significant
inefficiencies into the retail workflow. Customers often
experience extended wait times, especially during peak
hours, weekends, or holiday seasons, leading to potential
dissatisfaction and negative perceptions of the shopping
experience1. Moreover, the human element in the checkout
process increases susceptibility to errors—misidentified
products, incorrect pricing, or overlooked items—
culminating in discrepancies that may compromise both
retailer profitability and customer trust.
In light of evolving global dynamics, such as the COVID-19
pandemic, the imperatives of the traditional checkout
process have been further accentuated. The heightened
emphasis on health and safety protocols necessitates
strategies to minimize human interactions, reduce
touchpoints, and implement contactless transaction
mechanisms. Consequently, there is an escalating demand
for automated, efficient, and hygienic solutions that align
with contemporary health guidelines and consumer
preferences.
2.2 Technological Advancements in Retail
The retail landscape is undergoing a transformative
metamorphosis, propelled by rapid advancements in
technology and innovation. Recent literature elucidates the
burgeoning influence and adoption of AI, computer vision,
and deep learning technologies within retail ecosystems,
heralding a new era of operational excellence, consumer
engagement, and market differentiation2.
2.2.1 Artificial Intelligence (AI) in Retail:
AI technologies, characterized by machine learning
algorithms and predictive analytics, are redefining retail
dynamics. Retailers leverage AI-driven insights to optimize
inventory management, personalize customer experiences,
and forecast market trends. Moreover, AI-poweredchatbots
and virtual assistants streamline customer interactions,
facilitate real time support, and enhance engagement,
fostering loyalty and satisfaction.
2.2.2 Computer Vision and Enhanced Visual
Capabilities: The integration of computer vision
technologies revolutionizes product recognition, quality
control, and security surveillance within retail
environments. Advanced algorithms facilitate automated
item identification, anomaly detection, and real-time
monitoring, ensuring accuracy, efficiency, and compliance
with industry standards.
2.2.3 Deep Learning and Data-Driven Decision Making:
Deep learning methodologies, notably Convolutional Neural
Networks (CNNs), empower retailers to extract intricate
patterns, insights, and correlations from vast datasets. By
analyzing consumer behavior, preferences, and purchasing
patterns, retailers can tailor marketing strategies, optimize
pricing strategies, and curate personalized product
recommendations, thereby maximizing revenue potential
and enhancing customer satisfaction.
3. BLOCK DIAGRAM
Fig.1:Block Diagram of AI Autobilling System
WORKFLOW EXPLANATION:
User: This is the individual initiating the process by
interacting with the system. The user journey begins from
this point.
Home Page: Purpose: Serves as the main interface where
users can start their shopping or transaction journey.
Interaction: From here,userscannavigatetoselectproducts,
view offers, or proceed to other functionalities.
Empty Data Check: Purpose: This is a checkpoint to
determine if the user has added any items to their cart or if
any data is present.
LED (Light Emitting Diode):
No Data (No): If the system detects an empty cart or no
selected items, an LED might be triggered to indicate this
status. This could visually prompt the user to add products
to their cart.
Data Present (Yes): If items are in the cart or data ispresent,
the workflow proceeds to the next stages.
Load Cell: Purpose: It's involved in measuring weight or
force, likely related to the items placed in the cart.
Interaction with Raspberry Pi: The Load Cell communicates
its measurements or data to the Raspberry Piforprocessing,

ensuring accurate weight-based functionalities like pricing
or inventory management.
Raspberry Pi: Purpose: Actsasthecentral huborprocessor,
orchestrating interactions between various components.
Interaction with Camera Module: The Raspberry Pi receives
data or commands fromtheCamera Module,possiblyrelated
to product identification, scanning, or visual recognition
tasks.
Interaction with Reat API: The Raspberry Pi communicates
with the Reat API, facilitating data exchange, processing
requests, or integrating with external systems and services.
Camera Module: Purpose: Captures visual data, facilitating
tasks such as product identification, QR code scanning, or
other visual interactions. Interaction with Raspberry Pi:
Sends captured images or visual data to the Raspberry Pifor
processing, analysis, or further actions.
Reat API: Purpose: An Application Programming Interface
(API) that facilitates communication, data exchange, or
integration with external systems.
Interaction with Raspberry Pi: The Reat API interacts with
the Raspberry Pi, possibly providing data processing
capabilities, accessing databases, or enabling functionalities
like payment processing, product information retrieval, etc.
Checkout Page: The checkout interface has two parts,
1. Front-end developed using HTML, JS
2. Backend API developed using NodeJS and Express
1. Front-end developed using HTML, JS The front-end
continuously checks for the changes happening in the back-
end API and displays the changes to the user. Once anitemis
added to the API, the front-end displays as an item added to
the cart.
2. Backend API developed using NodeJS and Express The
backend REST API is developed using NodeJS and Express.
ExpressJS is one of the most popular HTTP server libraries
for Node.js, which ships with very basic functionalities. The
backend API keeps the details of the products that are
visually identified.
QR API:Purpose: Handles QR (Quick Response) code-
related functionalities, including scanning, processing, or
validation. InteractionwithPayment Success:Afterscanning,
the QR API validates the information, processes the
transaction, and triggers a payment confirmation.
Payment Success:Purpose: Indicatessuccessful completion
of the payment transaction.
Interaction with Home Page (Return): After successful
payment, users are redirected or returnedtotheHomePage,
completing the transaction cycle and potentially initiating
subsequent interactions.
4. COMPONENT DESCRIPTIONS
4.1 Hardware Requirements:
1. Raspberry Pi 4 module B
Description: This is a versatile single-board computer (SBC)
equipped with powerful processing capabilities, offering
enhanced performance compared to its predecessors.
Functionality: Acting as the brain of the system, it manages
various tasks, including data processing, communication
with peripherals, and executing software algorithms. Its
GPIO (General Purpose Input/Output) pins facilitate
hardware interfacing and control.
2. Weight Sensor (Load Cell) 0-10k:
Description: A precision instrument designed to measure
weight or force within a specific range (0-10kg).
Functionality: When integrated into the shopping cart or
platform, it detects the weight of products, translating force
exerted onto the sensor into measurable electrical signals.
This data aids in determining the cost of items based on
weight and managing inventory levels.

3. SparkFun Load Cell Amplifier
Description: A specialized amplifier tailored to enhance the
sensitivity and accuracy ofsignalsderivedfromtheLoadCell
sensor.
Functionality: It amplifies weak signals from the Load Cell,
compensating for variations and ensuring consistent and
precise weight measurements. This amplified data is then
transmitted to the Raspberry Pi for further processing and
analysis.
4. 5V 2A Power Supply
Description: A dedicated power source providing5volts at2
amperes, suitable for powering the Raspberry Pi and
associated components.
Functionality: Ensures stable and uninterrupted power
supply, safeguarding against voltage fluctuations or
inconsistencies that could compromise systemperformance
or reliability.
5. REES52 5 Megapixel 160 deg. Wide Angle Fish-Eye
Camera
Description: A sophisticated camera module boasting a 5-
megapixel resolution and a 160-degree wide-angle fish-eye
lens.
Functionality: Capable of capturing high-definition images
with an expansive field of view, it supports various
applications such as product recognition, barcode scanning,
and visual interactions. The wide-angle lens facilitates
comprehensive coverage, reducing blind spots and
enhancing system efficiency.
6. RGB Led Strip
Description: An array of LED lights featuringRed,Green,and
Blue color channels, enabling a spectrumofcolorsandvisual
indications.
Functionality: Used for displaying status indicators,
notifications, or user prompts through distinct color-coded
signals.
For instance, different colors could signifysystemreadiness,
error states, or specific user actions, enhancing user
experience and interaction.
7. Plywood
Description: A material used for constructing the physical
structure or housing of the system.
Functionality: Provides a sturdy and customizable base or
enclosure for mounting components and ensuring system
stability.
8. Wire
Description: Electrical cables or wires used for connecting
and integrating various hardware components.
Functionality: Enables data transmission,powersupply,and
communication betweeninterconnectedhardwaremodules.
9. DC Power Supply
Description: Supplies direct current (DC) electrical powerto
the system components.
Functionality: Ensures appropriate voltage and current
levels required for the operation of the Raspberry Pi,
sensors, and other peripherals.
10. Core i3 Processor
Description: A central processing unit (CPU) providing
computational capabilities for specific tasks or
functionalities.
Functionality: Supports data processing, system operations,
and software execution within the designated environment.
11. 8GB RAM
Description: Random Access Memory (RAM) providing
temporary storage and quick access to data for processing
tasks.
Functionality: Enhances system performance, multitasking
capabilities, and software execution efficiency by providing
sufficient memory resources.

4.2 Software Requirements:
1. Python 3.11.1
Description: A versatile and high-level programming
language renowned for its simplicity, readability, and
extensive library support.
Functionality: Facilitates the development, deployment,and
execution of AI algorithms, data processing scripts, and
system functionalities within the AI-based auto billing
environment. Its dynamic typing and object-oriented
features enable rapid prototyping, integration, and
optimization of software components.
2. Edge Impulse Studio
Description: An innovative platform specializing in the
creation, training, and deployment of edge AI models
tailored for
embedded systems and IoT devices.
Functionality: Empowers developers to design custom
machine learning algorithms, integrate sensor data, and
deploy optimized models within the Raspberry Pi
environment. With built-in tools, libraries, and cloud-based
services, it
streamlines the AI development lifecycle, from data
collection to model optimization and deployment.
3. Raspberry Pi Raspbian
Description: The official operating system optimized for
Raspberry Pi devices, built upon the Debian Linux
distribution.
Functionality: Offers a stable, secure, and user-friendly
environment tailored for embedded computing, IoT
applications,
and educational purposes. It provides essential utilities,
software packages, and system configurations, ensuring
seamless
integration, compatibility,andperformanceoptimization for
Raspberry Pi-based projects.
4. Microsoft Visual Studio Code
Description: A lightweight, extensible, and cross-platform
source code editor developed by Microsoft, designed for
modern software development workflows.
Functionality: Offers a rich set of features, extensions, and
integrations, supporting code editing, debugging, version
control, and collaboration. Its intuitive interface,
customizable settings, and integrated development
environment (IDE) capabilities facilitate Python script
development, AI algorithm implementation, and system
configuration tasks within the AI-based auto billing system
environment.
5. CORE TECHNOLOGIES
5.1 Computer Vision Technology
Computer Vision (CV) is a field of artificial intelligence that
enables machines to interpret and make decisions based on
visual data, much like human vision. In the context of the AI-
based autobilling system,computervisiontechnologyallows
the system to recognize, detect, and analyze retail items
without human intervention. This facilitates automated
checkout processes, enhances efficiency,reduces errors,and
improves overall customer experience.
Convolutional Neural Network (CNN):
Convolutional Neural Networks (CNNs) are a class of deep
learning algorithms specifically designed for processing
visual data such as images and videos. CNNs have
revolutionized the field of computer vision due to their
ability to automatically and adaptively learn spatial
hierarchies of features from input images. Here's how CNN
operates:
Steps Involved in CNN Algorithm:
Input Layer: The process begins with feeding an image into
the CNN. This image is represented as a matrix of pixel
values, forming the input layer of the neural network.
Convolution Operation: CNNs employconvolutional layers
that apply convolution operations using filters (also known
as kernels) across the input image. These filters extract
various features like edges, textures,shapes,andpatterns by
sliding or convolving across the input image andperforming
element-wise multiplication and summation operations.
Activation Function: Following convolution, an activation
function, typically Rectified Linear Unit (ReLU), introduces
non-linearity to the model by transforming the convolved
feature maps, enabling the network to learn complex
representations and relationships within the data.
Pooling Layer: After activation, pooling layers, commonly
Max Pooling, downsample thefeaturemaps, reducingspatial
dimensions while preserving essential information. Pooling
enhances computational efficiency, reduces overfitting, and
focuses on salient features extracted during the
convolutional process.
Flattening: Post pooling, the feature maps are flattenedinto
a one-dimensional vector, preparing the data for input into
fully connected layers. This flatteningconsolidatesextracted
features, facilitating higher-level pattern recognition, and
classification tasks.
Fig.2:Convolutional Nrural Network

Fully Connected Layers: The flattened features propagate
through fully connected layers or neurons, performing
intricate computations to classify the input image into
specific categories or labels.
These layers integrate spatial hierarchies, contextual
information, and feature representations,enablinginformed
decision-making and precise object identification.
Softmax Activation: The final layer employs the softmax
activation function, computing probabilities associatedwith
potential product categories or labels.
This probabilistic output signifies the network's confidence
levels regarding the product's identity, facilitating accurate
classification and subsequent billing processes within the
autobilling system.
5.2 Weight Sensing Technology
In this Project, weight sensing technologyplaysa crucial role
in ensuring accurate billing for products sold by weight.
Here's a concise overview of its implementation:
Hardware Integration: Incorporate specialized weight
sensors like load cells with the checkout platform,
connecting them to a Raspberry Pi for real-time data
processing.
Real-Time Measurement & Integration: Continuously
monitor product weights and integrate this data with AI
algorithms. This ensures precisebillingalignedwith product
identification and pricing information.
Automated Billing: Enable synchronized operation between
weight sensors and AI systems for automated, accurate
billing calculations. This facilitates dynamic pricing
adjustments and promotes transparent billing practices.
Quality Assurance & Compliance: Maintain and calibrate
weight sensors regularly to ensure accuracy. Adhere to
industry standards and regulatory compliance to foster
consumer trust and mitigate potential discrepancies.
By seamlessly integrating weight sensingtechnology,the AI-
based auto billing system enhances accuracy, efficiency,and
consumer satisfaction, ensuring precise billing for weight-
based products and promoting trust within the retail
environment.
6. RESULTS AND DISCUSSION
6.1 Experimental Framework and Integration
The AI-based autobilling system was meticulously designed
to accommodate a diverse range of products, encompassing
staples such as potatoes, consumableslikewaterbottles, and
indulgent items such as chocolates. Leveraging advanced
technologies like computer vision,deeplearning,and weight
sensing, the system adeptly harmonized product
identification, weight assessment, and pricing mechanisms,
ensuring a streamlined and efficient retail experience.
6.2 Comprehensive Billing Results
Upon system activation, the practicality and efficacy of the
AI-based autobilling system were prominently showcased
through its seamless integration and precise product
identification capabilities. As illustrated in Table 1 below,
each product category was accurately assessed, priced, and
billed, reflecting the system's proficiency and adaptability.
Fig.(a) Fig.(b) Fig.(c)
Object Detection
Table -1: Automated Billing Utilizing Advanced
Technologies
Column A (Product Name): Specifies the distinct products
incorporated within the billing system, encompassing
staple items like potatoes, consumables such as water
bottles, and indulgent treats like chocolates.
Column B (Per Kg/Unit Price in Rs): Denotes the cost per
kilogram for potatoes and the unit price for water bottles
and chocolates, set at Rs 20/kg for Potatoes, Rs 30 per unit
for Water Bottles, and Rs 50 per unit for Chocolates.
Column C (Quantity): Indicates the respective quantities of
each product type available for billing, encompassing3units
of Potatoes, 2 units of Water Bottles, and 5 units of
Chocolates.
Column D (Measured Weight in kg/g): Represents the
precise weight or quantity measured for each product
category, detailing 1.5 kg for Potatoes, 1.0 kg for Water
Bottles, and 0.250 kg for Chocolates.
Column E (Total Cost in Rs): Calculates the cumulative cost
for each product category based on the measured
weight/quantity and correspondingpricing,culminatingina
comprehensive total of Rs 340.

This Excel column-like representation exemplifies the AI-
based autobilling system'sadeptnessinharmonizingdiverse
product categories, facilitating precise weight assessment,
pricing computations, and streamlined billing processes,
thereby ensuring operational efficiency and consumer
satisfaction across varied retail offerings.
6.3 Discussion and Implications
The empirical results substantiate the AI-based autobilling
system's transformative capabilities within intricate retail
scenarios. By seamlessly integrating diverse product
categories and employing advanced technological
frameworks, the system exemplifies innovation, efficiency,
and adaptability. The precise billing, product identification,
and dynamic pricing mechanisms underscore the system's
potential in fosteringconsumertrust,operational excellence,
and market relevance.
7.CONCLUSION
The AI-based autobilling systemsignifiesa monumental leap
in retail technology, seamlessly amalgamating computer
vision, deep learning, and weight sensing capabilities to
redefine the checkout experience. Through empirical
evaluations, the system has showcased unparalleled
proficiency in preciseproductidentification,accurateweight
assessment, and dynamic pricing, fostering enhanced
consumer trust and operational efficiency across diverse
product categories. Emphasizing accuracy, transparency,
and adaptability, the system transcends traditional retail
paradigms, offering a streamlined, efficient, and consumer-
centric approach. As a pioneering solution, the AI-based
autobilling system elucidates promising avenues for future
research and technological refinements, positioning
stakeholders at the nexus of innovation, resilience, and
growth within the dynamic retail landscape.
References
1.https://ieeexplore.ieee.org/abstract/document/6144946
2.https://iopscience.iop.org/article/10.1088/1757-
899X/1084/1/012035/meta
3. https://ieeexplore.ieee.org/abstract/document/8697995
4.https://openaccess.thecvf.com/content_cvpr_2017/html/
Redmon_YOLO9000_Better_Faster_CVPR_2017_paper.html
5.https://ieeexplore.ieee.org/abstract/document/669712
5

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