A Project Report Submitted in Partial Fulfilment of

The Requirements for the degree of

Master of Computer Application

On
CURRENCY RECOGNITION SYSTEM
Of
PROJECT
KCA451
MCA -II YEAR /III Semester
Submitted by

Simran Dwivedi 2301640140138

Bhoomika Awasthi 2301640140062
Pulkit Srivastava 2301640140138

UNDER THE SUPERVISION OF

Dr. Gyanendra Kumar Gupta
(Professor)

PRANVEER SINGH INSTITUTE OF TECHNOLOGY, KANPUR

To the

Dr. A.P.J. Abdul Kalam Technical University, Lucknow

(2024-2025)
 CERTIFICATE

This is to certify that the work entitled Currency Recognition System

being presented in this project by Bhoomika Awasthi (2301640140062), for the
partial fulfilment of Master of Computer Applications, from Pranveer Singh
Institute of Technology, Kanpur affiliated to Dr. A.P.J. Abdul Kalam Technical
University, Lucknow, is a bonafide record of his own work carried during the
academic year 2024-25.

Dr. Gyanendra Kumar Gupta Dr. Rahul Deo Shukla

(Professor) (Head of Department)
 DECLARATION

I hereby declare that the project work entitled “Currency Recognition System”
submitted to the MCA Department, PSIT Kanpur. It is a record of an original work
done by me under the guidance of Dr. Gyanendra Kumar Gupta, Professor, MCA,
and this project work is submitted in the partial fulfilment of the requirements for
the award of the degree of Master of Computer Application. The results embodied
in this project have not been submitted to any other University or Institute for the
award of any degree or diploma.

DATE : Name: Bhoomika Awasthi

Roll no: 2301640140062

Section: MCA 2A
 ACKNOWLEDGEMENT

We would like to acknowledge all whose guidance and encouragement that has made
us to do what is done so far, we avail the opportunity to express our deep sense of
gratitude and sincere thanks to our college. We express our sincere gratitude to Dr.
Rahul Deo Shukla, Head of Department, MCA, PSIT, Kanpur for providing us the
facilities.

We take this opportunity to express our heartfelt sincere thanks to Faculty of MCA,
whose encouragement and best wishes provided impetus for this project. Our sincere
gratitude to our Supervisor, Dr. Gyanendra Kumar Gupta, Professor, Department
Of Computer Application, for this valuable suggestion and giving us permission to
commence this project in the first instance, to do the necessary research work and to
use the organizational data.

Last but not the least we also thank to our parents for being supportive in all our
activities and carrier without whom it wouldn’t be possible for us to reach successful
completion of this project.

With gratitude ,

Bhoomika Awasthi

2301640140062

MCA-2A
 ABSTRACT

The Currency Recognition System is a cutting-edge solution designed to accurately

identify various currencies and denominations, facilitating seamless transactions and
improved accessibility for individuals and organizations. Utilizing advanced image
processing and machine learning algorithms, this system quickly and accurately detects
currency notes from multiple countries
This project not only enhances the ease of currency management but also offers a secure
and efficient way to verify authenticity, helping reduce fraud and error in financial
transactions. Deployed as a mobile app, desktop software, or integrated system for point-
of-sale terminals, this recognition system promises an intuitive user experience and broad
applicability across different sectors.
Hardware Requirements
Processor: Dual-Core 2.0 GHz or higher,RAM: Minimum 4 GB (8 GB recommended for
smoother performance),Storage: 20 GB of free storage space,Camera: A built-in or
external camera capable of capturing clear images (for real-time detection),Operating
System: Compatible with Windows or Linux.
Software Requirements
IDE/Code Editor: Visual Studio Code or PyCharm for writing and managing code,
Programming Language: Python (since you are using Python for this project).
Libraries:OpenCV: For image capture and processing , TensorFlow/PyTorch: For machine
learning and neural network implementation , Numpy: For numerical operations and data
handling , Imutils: (Optional) For easier image processing
Database: SQLite (or any lightweight database) if you need to store results or history of
recognized currencies , Pre-trained Model: A pre-trained model for currency detection,
which you could find on platforms like Kaggle or train yourself if you have sufficient data.
 INDEX
CONTENT PAGE NO.

I. Chapter:-1
• Introduction 8
➢ Problem Definition 9
➢ Purpose 10
➢ Hardware and software specification 12
➢ Problem Statement 14
➢ Proposed Solution 15
➢ Scope 16

II. Chapter:-2
• Project analysis 18
➢ Study of Existing system 19
➢ Gap In Study 21
➢ Feasibility study 23
➢ Tools used to gather information 26

III. Chapter:-3
• Project Design
➢ Software requirement specification 29
➢ Software functional specification 31
➢ Data flow diagram 33
➢ E-R diagram 36
➢ UML diagrams 39
IV. Chapter:-4
• System implementation 45
V. Chapter:-5
➢ Testing 56
➢ System input and output screenshot 58
➢ Limitations and Scope of project 66
VI. Conclusion 70

VII. References 71
Chapter:-1
 INTRODUCTION

Currency recognition is an essential and innovative application of technology that

addresses real-world needs across various industries. The Currency Recognition System
project leverages the power of deep learning to create an intelligent solution for identifying
Indian currency. From simplifying cash transactions to enhancing financial operations, this
system is designed to provide an interactive and efficient experience for users.
At its core, this project focuses on the seamless recognition and interpretation of Indian
currency denominations. The process involves employing advanced algorithms and
models to analyze images of currency notes captured through a camera, ensuring accuracy
and reliability in real time. The system not only identifies the denomination but also
delivers detailed information about the note, offering both convenience and educational
value.
The development of the Currency Recognition System brings together a combination of
programming, data analysis, and artificial intelligence. With Python as the foundation, and
frameworks such as PyTorch, TensorFlow, and Matplotlib, the project exemplifies the
integration of cutting-edge technologies to solve practical challenges. These tools facilitate
the design, training, and optimization of robust models capable of adapting to various
conditions, including different lighting and angles.
This project embodies creativity, technical skill, and problem-solving. It reflects the
potential of AI to transform day-to-day operations in sectors like banking, retail, and
education. Whether it's aiding financial institutions in streamlining cash handling or
serving as an educational tool, the Currency Recognition System showcases how
technology can bridge gaps and make processes more efficient.
By combining technical expertise with a vision for practical utility, this project contributes
to the evolving field of artificial intelligence and its applications. The Currency
Recognition System is not just a tool but a step forward in harnessing technology for
smarter, faster, and more accurate solutions.

8
 PROBLEM DEFINITION

Currency Recognition System Using Image Processing

The goal of this project is to develop an automated currency recognition

system that utilizes image processing techniques to identify and classify
various currencies based on their visual features. The system will take an
image of a banknote as input and accurately determine the denomination
and authenticity of the currency. This system aims to eliminate the need for
manual inspection and improve efficiency in environments such as banks,
vending machines, and ATMs.

The key challenges include:

Variation in Currency Design: Different currencies, even within the same

denomination, may have subtle differences in design due to variations in
print quality, security features, and wear-and-tear over time.

Environmental Factors: Variations in lighting conditions, image quality, and

background noise can affect the performance of the system, requiring robust
preprocessing techniques.

Multiple Currencies: The system should be capable of recognizing various

types of currencies (e.g., USD, EUR, INR) by distinguishing between
different designs, colors, and security features.

Security and Authentication: The system must not only recognize the
currency but also identify counterfeit notes by detecting hidden security
features such as watermarks, holograms, and microprinting.

Real-time Processing: The recognition system must process and classify the
currency in real time or near real-time to be applicable in practical
applications such as ATMs, cash handling machines, and point-of-sale
systems

9
 PURPOSE

The Currency Recognition System is designed to transform the way Indian currency is
identified and utilized by offering a seamless, efficient, and intelligent solution. This
project focuses on addressing key challenges in currency handling, authentication, and
education using the power of deep learning.
Key objectives of the project include:
• Enhancing Accuracy: Providing a reliable solution to accurately identify Indian
currency denominations in real time, minimizing human error.
• Streamlining Transactions: Simplifying cash handling in banking, retail, and other
financial operations by automating the recognition process.
• Educating and Empowering Users: Demonstrating the practical applications of AI
and deep learning, making it a valuable tool for educational and training purposes.
• Delivering Real-Time Insights: Offering instant recognition and detailed
information about currency notes for improved user experience.
With its emphasis on innovation and real-world utility, the Currency Recognition System
aspires to:
• Serve as an efficient tool across various sectors such as banking, retail, and
education.
• Demonstrate the potential of artificial intelligence in solving everyday challenges.
• Promote accessibility and user-friendliness to ensure widespread adoption and
impact.
This project is a testament to the transformative power of technology, bridging the gap
between artificial intelligence and practical problem-solving to create a smarter, more
efficient future.

10
Social Interaction:

Games bring people together, fostering social interaction and collaboration

among players. Whether playing cooperatively to achieve shared goals or
competing against each other in friendly rivalry, multiplayer games provide
opportunities for social bonding and connection.

Escapism and Relaxation:

Games offer an escape from the stresses and challenges of everyday life,
allowing players to unwind, relax, and immerse themselves in virtual worlds of
their choosing. Whether exploring a vast open world, solving puzzles, or
engaging in casual game play, games provide a form of entertainment that can
be both stimulating and therapeutic.

Commercial Success:

Game development is also driven by commercial considerations, with

developers aiming to create successful products that generate revenue and
profit. This may involve targeting specific market niches, leveraging popular
trends, and adopting effective monetization strategies to ensure the financial
viability of the project.

Tracking and evaluating body movements for training purposes, biomechanical

analysis, and injury prevention.

11
 HARDWARE AND SOFTWARE SPECIFICATION:

➢ Hardware Specifications:

❖ Processor (CPU): A multi-core processor with high clock speeds

is recommended for training and running deep learning models.
Processors such as Intel Core i7/i9 or AMD Ryzen 7/9 are well-
suited for the computational demands of this project.
❖ Graphics Card (GPU): A dedicated GPU is essential for
accelerating deep learning computations. NVIDIA GPUs with
CUDA support (e.g., NVIDIA GeForce RTX 3060 or higher) are
ideal for training and testing models.
❖ Memory (RAM): Deep learning tasks can be memory-intensive. A
minimum of 16 GB RAM is recommended for smooth
multitasking, with 32 GB or more beneficial for handling larger
datasets.
❖ Storage: A solid-state drive (SSD) is recommended for faster data
access and training performance. An SSD with at least 512 GB
capacity is ideal, paired with an additional hard disk drive (HDD)
for bulk data storage.
❖ Camera: A high-resolution laptop or external camera is required
for capturing currency images in real-time for recognition tasks.
❖ Monitor : A high-resolution monitor with good color accuracy is
beneficial for visualizing training progress, testing results, and
debugging.

12
➢ Software Specifications:

❖ Programming Language: Python is the core programming

language for developing the project, chosen for its extensive
library support and ease of use in AI/ML applications.
❖ Deep Learning Frameworks:
• PyTorch: For designing, training, and testing neural
network models.
• TensorFlow: For implementing additional deep learning
functionalities and optimizing performance.
❖ Data Visualization Tools:
• Matplotlib: For visualizing data insights, training
accuracy, and loss metrics during model development.
❖ Integrated Development Environment (IDE): IDEs like Jupyter
Notebook, PyCharm, or Visual Studio Code are ideal for writing,
debugging, and testing Python scripts.
❖ Dataset Tools: Tools like Pandas and NumPy for data preprocessing
and handling structured currency data.
❖ Version Control System: Git for managing project code, tracking
changes, and collaborating efficiently.
❖ Operating System: Windows, macOS, or Linux-based systems with
support for Python and deep learning libraries.
❖ Camera Integration Tools: OpenCV for integrating and processing
real-time camera input for currency recognition.

13
 PROBLEM STATEMENT

The accurate identification and handling of currency notes remain a challenge in various
sectors such as banking, retail, and education. Manual processes are prone to errors, time-
consuming, and inefficient, especially when handling large volumes of cash or unfamiliar
denominations. Moreover, individuals with visual impairments or limited exposure to
currency details face significant barriers in identifying and using currency effectively.

Key Challenges:
• Accuracy and Efficiency:
Traditional methods of currency recognition rely heavily on manual inspection,
leading to potential errors and delays in operations such as cash handling and
transactions.
• Accessibility:
People with visual impairments or limited literacy may struggle to identify currency
denominations, making financial independence and inclusion difficult.
• Counterfeit Detection:
With counterfeit currency circulation on the rise, existing tools often lack the
precision or affordability required to effectively authenticate notes.
• Real-Time Recognition:
A reliable solution for real-time currency recognition using widely available
devices, like laptop cameras, is currently lacking in the market.

Need for the Solution:

The Currency Recognition System aims to address these challenges by leveraging deep
learning to create a robust and accessible tool for real-time Indian currency recognition.
By providing accurate, efficient, and user-friendly functionality, this project seeks to
streamline cash handling processes, promote financial inclusivity, and ensure counterfeit
detection.

14
 PROPOSED SOLUTION

To address the challenges outlined in the problem statement, the Currency Recognition
System aims to provide an accurate, efficient, and user-friendly tool for identifying Indian
currency in real time. The solution leverages deep learning technologies to ensure
reliability, accessibility, and scalability for diverse applications.

Accuracy and Efficiency Improvements:

1. Model Optimization:
o Develop and train robust deep learning models using frameworks like
PyTorch and TensorFlow for accurate currency recognition.
o Continuously improve model performance by incorporating larger datasets
and fine-tuning algorithms.

2. Image Preprocessing:
o Implement advanced image preprocessing techniques (e.g., noise reduction,
color correction) to ensure consistent results across different lighting
conditions and angles.

3. Real-Time Processing:
o Use optimized computer vision libraries like OpenCV to enable seamless
real-time recognition through laptop cameras or external devices.

4. Error Mitigation:
o Regularly test and validate the system to minimize false positives and
negatives, ensuring high reliability in diverse environments.

Accessibility and Inclusivity Enhancements:

1. User-Friendly Interface:
o Design an intuitive graphical user interface (GUI) that provides clear
instructions and visual/audio feedback for easy navigation and use.\

2. Voice Assistance:
o Integrate voice-based outputs to announce recognized currency
denominations, catering to users with visual impairments.

3. Language Support:
o Provide multilingual support to increase accessibility for users across
different regions of India.

15
Counterfeit Detection and Security:

1. Authentication Features:
o Incorporate counterfeit detection capabilities by training models to identify
specific security features (e.g., watermarks, micro-text, and color patterns) on
genuine currency notes.

2. Continuous Updates:
o Regularly update the system to adapt to new currency designs and security
enhancements introduced by regulatory authorities.

Scalability and Deployment:

1. Cross-Platform Compatibility:
o Ensure the system works seamlessly across different operating systems
(Windows, macOS, Linux) to cater to a broader audience.

2. Edge Computing:
o Optimize the system for lightweight deployment on edge devices, enabling
offline recognition without reliance on high-end hardware.

3. Integration Possibilities:
o Develop APIs to integrate the system with cash handling machines, point-of-
sale (POS) systems, or educational tools for wider applicability.

By addressing these aspects, the Currency Recognition System seeks to transform

currency identification processes, making them more accurate, accessible, and reliable for
a diverse user base.

16
 PROJECT SCOPE

The scope of the Currency Recognition System encompasses a range of activities,

disciplines, and considerations required to create a reliable, efficient, and user-friendly tool
for identifying Indian currency. Below are the key aspects of the project's scope:

Conceptualization and Design

• Define the project’s objective of real-time Indian currency recognition using deep
learning techniques.
• Outline the system’s core features, including real-time recognition, counterfeit
detection, and audio or visual outputs for accessibility.
• Design intuitive workflows and user interfaces tailored for seamless operation and
diverse user requirements.

Programming and Development

• Implement deep learning models using frameworks such as PyTorch and
TensorFlow to recognize and authenticate currency.
• Develop the application in Python, integrating libraries like OpenCV for real-time
image processing and Matplotlib for visualizing system performance metrics.
• Write code for GUI development and user interaction, ensuring smooth integration
of all functionalities.

Data Collection and Preprocessing

• Collect a diverse dataset of Indian currency images covering various denominations,
orientations, and lighting conditions.
• Preprocess the dataset using techniques such as noise reduction, cropping, and
normalization to optimize model training.

Model Training and Testing

• Train and fine-tune neural networks to achieve high accuracy in currency
recognition and counterfeit detection.
• Test the system rigorously using different scenarios to evaluate performance, speed,
and reliability under varying conditions.

Accessibility and User Experience

• Incorporate features such as voice outputs for recognized denominations to assist
visually impaired users.
• Offer multilingual support and ensure the interface is simple, intuitive, and
accessible for users across different demographics.

17
Integration and Deployment
• Develop a cross-platform application compatible with major operating systems
(Windows, macOS, Linux).
• Optimize the system for real-time deployment on standard laptops using built-in or
external cameras.
• Enable future scalability by designing APIs for integration with other systems like
ATMs or point-of-sale machines.

Quality Assurance and Testing

• Conduct extensive testing to identify bugs, ensure accuracy, and validate the
system’s performance under real-world conditions.
• Perform compatibility testing across various hardware configurations to ensure a
consistent user experience.
• Collect user feedback to iteratively refine and enhance the system.

The Currency Recognition System will provide a comprehensive, accessible, and

scalable solution, addressing critical challenges in currency identification and ensuring
widespread utility in sectors such as banking, retail, and education.

18
Chapter :-02

19
 PROJECT ANALYSIS

The Currency Recognition System is designed to accurately recognize and authenticate

Indian currency in real time using deep learning techniques. This analysis outlines the
project's objectives, requirements, design, and potential challenges.

Objectives
1. Currency Recognition: Develop a system capable of identifying Indian currency
denominations with high accuracy.
2. Counterfeit Detection: Integrate features to authenticate currency notes and detect
counterfeit ones.
3. Accessibility: Provide inclusive functionality, such as audio announcements and a
user-friendly interface, to cater to diverse users, including visually impaired
individuals.
4. Real-Time Performance: Enable real-time currency recognition using a standard
laptop camera or similar devices.
5. Scalability: Design the system for scalability, allowing future integration with other
applications like ATMs or point-of-sale systems.

Design
1. Client-Side Application:
o Develop a Python-based application with an intuitive graphical user interface
(GUI).
o Use frameworks such as PyTorch and TensorFlow for machine learning tasks.
2. Image Processing:
o Integrate OpenCV for real-time image capture and preprocessing to enhance
recognition accuracy.
3. Model Training:
o Train deep learning models using a diverse dataset of Indian currency images
for robust performance.
o Incorporate counterfeit detection by recognizing unique security features on
notes.
4. System Architecture:
o Build the application to work locally on laptops without reliance on high-end
hardware or internet connectivity.
o Ensure modularity for easy future enhancements

20
Features and Functionality

1. Recognition Mechanism:
o The system captures currency images via a camera and identifies
denominations in real time.
2. Accessibility Features:
o Audio output for recognized denominations to assist visually impaired users.
o Multilingual support for wider accessibility.
3. Counterfeit Detection:
o Verify security features like watermarks and patterns to authenticate genuine
currency.
4. User Feedback and Logs:
o Maintain a log of recognized currencies and provide users with actionable
feedback.

Potential Challenges

1. Dataset Diversity:
o Acquiring a comprehensive dataset covering different denominations,
orientations, lighting conditions, and wear levels of notes.
2. Model Accuracy:
o Balancing recognition accuracy and processing speed to ensure real-time
performance.
3. Counterfeit Variability:
o Training the system to detect counterfeits that may vary in quality and
replication techniques.
4. Hardware Limitations:
o Optimizing the system to run efficiently on standard laptops without requiring
high-end hardware.
5. User Experience:
o Designing an interface that is both simple for non-technical users and robust
enough for professional applications.

21
 ➢ Study of existing system:

Studying existing systems for currency recognition involves analyzing the tools,
frameworks, models, methodologies, and technologies used in similar applications. This
helps identify best practices and challenges and determine the most suitable approach for
your project. Below are the key aspects to consider:

Currency Recognition Models

Currency recognition systems often utilize deep learning models trained on image datasets
of currency notes. Examples include:
• Convolutional Neural Networks (CNNs): Widely used for image classification
and feature extraction in currency recognition.
• YOLO (You Only Look Once): Employed for real-time object detection, including
identifying currency features.
• TensorFlow and PyTorch Models: Popular frameworks for developing, training,
and deploying deep learning models tailored for image recognition tasks.

Studying these models involves understanding their architectures, training processes,

performance metrics, and real-world applicability to Indian currency recognition.

Image Processing Tools

Image preprocessing is a critical step in currency recognition systems. Tools like OpenCV
are used for:
• Enhancing image quality through noise reduction, cropping, and filtering.
• Feature extraction for detecting patterns, watermarks, and other security elements on
currency notes.
Studying these tools involves exploring their capabilities, preprocessing techniques,
and integration with deep learning models.

Hardware Requirements
Currency recognition systems must operate efficiently on accessible hardware. Studying
existing systems involves analyzing:
• Hardware-accelerated processing (using GPUs or TPUs) for training models.
• Deployment on devices with limited computational power, such as laptops or
mobile devices, while maintaining performance.

22
Accessibility Features
Accessibility is a key feature of modern recognition systems. Existing systems offer:
• Text-to-Speech (TTS): Used for vocalizing currency denominations, particularly
for visually impaired users.
• Multilingual Support: Providing outputs in various languages to cater to diverse
users.
Studying these features involves exploring APIs and libraries like gTTS or Microsoft
Azure TTS for integrating speech functionalities.

Counterfeit Detection Technologies

Many systems aim to detect counterfeit currency by analyzing security features such as:
• Holograms, watermarks, and micro-text.
• Ultraviolet (UV) and infrared (IR) markers.
Studying existing counterfeit detection technologies involves understanding
methods to train models for identifying these features under diverse conditions.

Development Frameworks and Tools

The technology stack for currency recognition systems includes:
• Deep Learning Frameworks: TensorFlow, PyTorch, or Keras for building robust
recognition models.
• Visualization Tools: Matplotlib and Seaborn for model performance evaluation.
• Python Libraries: NumPy, Pandas, and SciPy for data handling and preprocessing.
Studying these tools involves understanding their functionalities, ease of use, and
compatibility with the overall project pipeline.

Development Methodologies
Adopting a systematic development approach ensures efficient project execution.
Commonly used methodologies include:
• Agile: For iterative development, frequent testing, and continuous feedback.
• Scrum: Organizing tasks into sprints for focused development and timely delivery.
• Waterfall: A linear approach for projects with clearly defined requirements.
Studying these methodologies involves selecting the one best suited to the project’s goals,
timeline, and resource availability.

23
 ➢ Gap in existing system:
Identifying the gaps in existing currency recognition systems highlights areas where
your project can innovate and provide enhanced functionality. Below are key gaps in
current systems:
1. Accessibility for Diverse Users
• Existing Gap:
o Most currency recognition solutions lack comprehensive accessibility
features, such as support for visually impaired users, multilingual
interfaces, or simple voice feedback.
• Proposed Improvement:
o Include text-to-speech in regional languages and customizable
interfaces to cater to users with varying needs.
2. Real-Time Recognition Efficiency
• Existing Gap:
o Current systems often struggle with real-time processing on standard
hardware, leading to delays or inaccuracies.
• Proposed Improvement:
o Optimize deep learning models for real-time performance using
lightweight architectures compatible with low-power devices.
3. Dataset Diversity
• Existing Gap:
o Many systems rely on limited datasets, resulting in poor recognition
accuracy under varying conditions like lighting, angles, and wear-
and-tear on currency notes.
• Proposed Improvement:
o Create a diverse dataset of Indian currency that includes notes in
different conditions (crumpled, soiled) and under varied lighting
scenarios.
4. Counterfeit Detection
• Existing Gap:
o Few systems incorporate robust counterfeit detection capabilities,
limiting their effectiveness for financial institutions or high-security
applications.
• Proposed Improvement:
o Integrate counterfeit detection features based on fine-grained patterns
and holographic elements using advanced image processing
techniques.
5. Scalability and Adaptability
• Existing Gap:
o Existing solutions often lack scalability, making it difficult to
integrate support for new currency designs or denominations.
• Proposed Improvement:
o Develop modular algorithms that can be easily retrained or updated
to recognize new currency features without significant
24
 redevelopment.
6. Cross-Platform Compatibility
• Existing Gap:
o Many systems are limited to specific devices or platforms, reducing
their utility for a wide range of users.
• Proposed Improvement:
o Ensure compatibility with multiple platforms, such as desktop,
mobile, and cloud-based solutions, for maximum reach and
flexibility.
7. Cost of Implementation
• Existing Gap:
o High costs of existing hardware-based solutions make them
inaccessible to smaller businesses or individual users.
• Proposed Improvement:
o Utilize software-only solutions leveraging existing hardware (e.g.,
laptop webcams) to reduce costs and make the system widely
accessible.
8. Lack of Multilingual Support
• Existing Gap:
o Existing systems rarely offer multilingual support, alienating non-
English-speaking users.
• Proposed Improvement:
o Incorporate support for multiple Indian languages in both textual and
audio outputs.
9. Data Privacy and Security
• Existing Gap:
o Systems often overlook data privacy concerns, especially for
connected solutions that store or process user data remotely.
• Proposed Improvement:
o Ensure compliance with data privacy regulations like GDPR and
implement secure, on-device processing to minimize data risks.
10. User Training and Ease of Use
• Existing Gap:
o Current systems require technical knowledge or training to operate
effectively, limiting usability for non-tech-savvy individuals.
• Proposed Improvement:
o Develop an intuitive user interface with guided tutorials and easy-to-
understand instructions.

25
 ➢ Feasibility study:

A feasibility study evaluates the technical, financial, and operational aspects of the
Currency Recognition System to ensure its viability and success. Below are the key
components:

Market Analysis
• Objective: Assess the demand and potential user base for the system.
o Target Users: Banks, retailers, visually impaired individuals, and educational
institutions.
o Competitive Landscape: Evaluate existing currency recognition solutions
and identify gaps, such as limited real-time capabilities and lack of support
for Indian currency.
o Market Demand: Analyze trends indicating the increasing need for
digitization and automation in financial transactions.
o Distribution Channels: Consider software platforms like app stores, direct
licensing, or bundling with hardware.

Concept Evaluation
• Objective: Validate the project's uniqueness and practical appeal.
o Innovation: Emphasize real-time recognition, counterfeit detection, and
multilingual support for Indian currency.
o Feasibility: Assess the implementation of machine learning models using
PyTorch and TensorFlow to achieve accurate recognition and lightweight
processing.
o User Appeal: Include accessibility features like audio feedback and simple
user interfaces to enhance usability.

Technical Assessment
• Objective: Evaluate technical requirements and challenges.
o Development Tools: Use Python, PyTorch, and TensorFlow for machine
learning; OpenCV for image processing.
o Hardware: Ensure compatibility with standard laptop webcams and low-end
computing systems.
o Challenges: Address issues like varying lighting conditions, note wear-and-
tear, and real-time performance optimization.
o Scalability: Design modular systems to accommodate new denominations or
currency changes.

26
Resource Planning
• Objective: Estimate resources and develop a detailed plan.
o Human Resources: Require expertise in Python, machine learning, and
software development.
o Timeframe: Plan for 3–6 months, including dataset preparation, model
training, and testing.
o Budget: Include costs for development, testing, and potential third-party tools
or APIs.
o Partnerships: Consider collaborations with financial institutions or visually
impaired organizations for user feedback.

Technical Feasibility Study

• Objective: Ensure technical viability.
o Implementation: Use pre-trained models and optimize them for Indian
currency using transfer learning.
o Hardware Requirements: Operate on laptops with basic specifications to
ensure wide usability.
o Platform Compatibility: Build cross-platform compatibility for Windows,
macOS, and mobile platforms.

Market Feasibility Study

• Objective: Assess market potential and competition.
o Demand: Address the lack of comprehensive systems for Indian currency
recognition.
o Competitors: Benchmark features against existing solutions like currency-
detection apps or hardware scanners.
o Monetization: Explore freemium models, one-time purchase pricing, or
licensing agreements.

Financial Feasibility Study

• Objective: Evaluate financial aspects.
o Budget: Allocate funds for development, testing, and marketing.
o Revenue Streams: Monetize through app sales, subscriptions, or institutional
licensing.
o Return on Investment (ROI): Estimate profitability based on potential user
adoption rates.

Operational Feasibility Study

27
 • Objective: Ensure smooth execution and project management.
o Team Expertise: Leverage team skills in machine learning, UI/UX design,
and software testing.
o Development Workflow: Use Agile methodologies for iterative development
and quick feedback cycles.
o Collaboration Tools: Utilize GitHub for version control and Trello for task
management.

Legal and Regulatory Feasibility Study

• Objective: Ensure compliance with legal requirements.
o Data Privacy: Adhere to GDPR and Indian data protection laws.
o Copyrights: Ensure proper licensing for pre-trained models and open-source
libraries.
o Currency Standards: Verify compliance with Reserve Bank of India (RBI)
guidelines.

28
29
 ➢ TOOLS USED TO GATHER INFORMATION

Tools Used to Gather Information

To develop the Currency Recognition System, various tools and resources are employed
to gather information, research, design, and implement the project. Below are the key tools
and their applications:

Online Research
• Objective: Collect information on similar projects, machine learning techniques,
and deep learning models.
o Resources: Use search engines, forums, and dedicated websites such as
Kaggle (for datasets), Stack Overflow (for technical queries), and GitHub (for
open-source projects).
o Communities: Engage with developer communities like TensorFlow Forums
and PyTorch Discussion Boards to gain insights and solutions for specific
challenges.

Documentation and Tutorials

• Objective: Learn about the frameworks, libraries, and techniques used in the
project.
o Sources:
▪ TensorFlow and PyTorch documentation for understanding the
libraries’ functionalities.
▪ OpenCV documentation for image processing techniques.
▪ Python official documentation for standard library features.
▪ Tutorials from platforms like Coursera, YouTube, and Towards Data
Science for step-by-step learning.
o Benefits: Ensure best practices in model implementation and optimization.

Development Tools
• Objective: Build and implement the system's features.
o Frameworks and Libraries:
▪ TensorFlow: For training and deploying machine learning models.
▪ PyTorch: For fine-tuning pre-trained models and building custom
models.
▪ OpenCV: For real-time image capture and processing.
o IDE/Editor: Use Google Colab for running experiments and Python IDEs
like PyCharm for local development.
o Version Control: Git and GitHub for managing code and collaboration.

Market Research Tools

• Objective: Analyze the system's demand and competitive landscape.
o Resources:
30
 ▪ Surveys and interviews with potential users, including financial
institutions and visually impaired individuals.
▪ Analysis tools like Google Trends to identify the demand for currency
recognition solutions.
o Benefits: Identify target users’ needs and refine features based on market
demand.

Prototyping and Testing Tools

• Objective: Validate the system design and performance.
o Prototyping Tools:
▪ Figma or Adobe XD for designing the user interface.
o Testing Tools:
▪ Google Colab's runtime environment for debugging and testing models.
▪ Real-time webcam testing using OpenCV for evaluating system
accuracy under various conditions.
▪ Jupyter Notebooks for step-by-step analysis and visualizing results.

Dataset Sources
• Objective: Gather high-quality data for model training and testing.
o Resources:
▪ Kaggle datasets featuring Indian currency images.
▪ Custom data collection using laptop cameras for real-world samples.
▪ Tools like LabelImg for annotating datasets.

31
32
33
34
Chapter:- 03

35
 PROJECT DESIGN

o Software requirement specification:

A Software Requirements Specification (SRS) for game development outlines the
functional and non-functional requirements of the game, providing a detailed
description of its features, capabilities, and constraints.
Here's an outline of the key sections typically included in an SRS for
game development:

1. Introduction
1.1 Purpose
The system helps identify Indian currency notes in real-time
using a laptop camera. It aims to assist users in quickly
detecting denominations.
1.2 Scope
The system uses machine learning to recognize Indian
currency. It is useful for visually impaired individuals,
businesses, and educational purposes.
1.3 Definitions, Acronyms, and Abbreviations
ML: Machine Learning
OCR: Optical Character Recognition

1.4References
No references were used during the design of this SRS.
1.5 Overview

The SRS outlines functional, non-functional, and interface

requirements for the system.
___________________________________________________

2. Overall Description
2.1 Product Perspective
The system integrates with a laptop camera, processes
images, and uses pre-trained ML models for real-time
36
 recognition.
2.2 Product Functions
Captures live images of currency notes.
Recognizes denomination and displays results.
Alerts users if the note is counterfeit.
2.3 User Characteristics

The system is user-friendly and does not require technical

knowledge. It is designed for everyday users and visually
impaired individuals.
2.4 Constraints
Requires a functional camera.
Accurate under good lighting.
Limited to Indian currency.
2.5 Assumptions and Dependencies
Assumes clear visibility of currency notes.
Depends on TensorFlow and compatible hardware.

3. Specific Requirements
3.1 Functional Requirements
Capture Image: Use the laptop camera to capture images.
Process Image: Recognize the denomination using pre-
trained ML models.
Display Result: Show the denomination on the screen.
3.2 Non-Functional Requirements
Performance: Response time under 2 seconds.
Usability: Simple UI for all users.
37
 Security: No storage of sensitive data.
3.3 External Interface Requirements
User Interface: A window displaying the live camera feed
and results.
Hardware Interface: Laptop camera with minimum 720p
resolution.

4. Design Constraints
The system must run on Python 3.7+ and TensorFlow 2.x.
Works best on systems with GPU for faster processing.

5. Other Non-Functional Requirements

5.1 Reliability
The system should handle multiple currencies consecutively
without crashing.

5.2 Portability
Should work on both Windows and Linux operating systems.

5.3 Maintainability
Regular updates for adding new currency notes or improving
detection accuracy.

38
 ➢ Software Functional Specification:

Introduction
This document defines the functional requirements of the Currency Recognition System.
It provides a detailed description of the system’s features, interactions, and expected
behavior to ensure clarity in development and implementation.

Scope
• Objective: To develop a real-time currency recognition system that identifies Indian
currency using a laptop camera and provides detailed information about the
denomination and features.
• Target Audience: Individuals with visual impairments, banking professionals, and
educational institutions.
• Platforms: Windows or Linux operating systems, compatible with standard laptop
configurations.
• Major Features:
o Real-time currency detection and recognition.
o Support for multiple denominations of Indian currency.
o User-friendly interface for visual and audio feedback.
o Ability to provide information about currency features, such as security marks
and dimensions.

Overall Description
System Concept
The system uses machine learning (ML) and computer vision (CV) techniques to identify
Indian currency in real-time. It leverages TensorFlow or PyTorch for ML model
deployment and OpenCV for image processing.
System Flow
1. Launch: Start the application via a desktop shortcut or command line.
2. Camera Activation: Automatically activates the laptop’s camera to capture live
video feed.
3. Currency Recognition:
o Detect currency in the camera frame.
o Classify the denomination using the trained ML model.
4. Feedback:
o Display the recognized denomination on the screen.
o Provide optional audio feedback for visually impaired users.

39
 5. Additional Information:
o Display security features or fun facts about the detected currency.
6. Exit: Close the application.
User Roles
• End Users: Individuals using the system for currency recognition.
• Administrators/Developers: Responsible for system updates, model training, and
performance tuning.

Functional Requirements
Input Methods
• Laptop camera for live video input.
• Mouse or keyboard for user interactions.
Core Functionalities
1. Currency Detection:
o Identify the presence of Indian currency in the camera feed.
o Locate and crop the detected currency for analysis.
2. Denomination Classification:
o Use a pre-trained deep learning model to classify the denomination (e.g., ₹10,
₹20, ₹50, etc.).
3. Information Display:
o Show recognized denomination on the UI.
o Provide optional audio feedback for better accessibility.
4. Error Handling:
o Notify users if no currency is detected or if recognition fails.
o Provide tips for proper camera alignment.
Use Cases
• Scenario 1: A user holds a ₹100 note in front of the camera.
o Expected Outcome: The system detects and classifies the note as ₹100 and
provides information via text and audio.
• Scenario 2: A non-Indian currency or invalid input (e.g., a plain piece of paper) is
presented.
o Expected Outcome: The system notifies the user of an invalid input.
Functional Dependencies
• Dependency on:
o Laptop camera hardware.
o Trained ML model and dataset.
o OpenCV for image processing.
o TensorFlow or PyTorch for model inference.

40
Assumptions and Constraints
• Assumptions:
o The system is used in a well-lit environment for accurate recognition.
o Users have basic familiarity with operating the system.
• Constraints:
o Limited to Indian currency recognition.
o Requires a laptop with a minimum of 4GB RAM and a functional camera.
o Real-time performance is subject to hardware capabilities.

References
• TensorFlow and PyTorch official documentation for model deployment.
• OpenCV documentation for image processing techniques.
• Kaggle dataset for Indian currency images.

41
 DFD (Data Flow Diagram)
A Data Flow Diagram (DFD) is a graphical representation of the flow of
data within a system. Creating a Data Flow Diagram (DFD) for currency
recognition involves visualizing the flow of data within the development
process. It shows how data moves through different processes and data
stores. Here's a simplified DFD for recognizing currency :
Level 0 DFD:

Entities:
1. User: Provides the input (Indian currency image) to the system.
2. System (Run Recognition): The core process that performs currency recognition.
3. Data Store: Holds trained model data and additional information about the
currency.

DFD Description
• Input:
o The user inputs an image of the currency through the laptop camera or other
input devices.
• Processing:
o The system processes the image using a trained deep learning model
(PyTorch/TensorFlow) to identify the denomination.
o The system retrieves relevant information about the currency (e.g., value,
security features) from the data store.
• Output:
o The system provides the recognized denomination and additional details
about the currency to the user.

42
 Level 1 DFD:

The Level 1 DFD provides further decomposition of the system, breaking the main
process into distinct subprocesses while illustrating how inputs, processing, and
outputs interact. It shows more specific workflows and connections within the
system.

Inputs
1. Currency Image Input: Images captured by the laptop camera.
2. Pre-Trained Model: Deep learning model loaded for recognition tasks.
3. User Request: User actions like viewing results or accessing information.

Processes
1. Image Acquisition: Capture images of currency through the laptop camera.
2. Pre-Processing: Enhance images (e.g., resizing, filtering) for recognition.
3. Currency Recognition: Detect and classify the denomination using the pre-trained
deep learning model.
4. Information Retrieval: Provide additional details about the recognized currency.
5. Result Display: Present identified currency and supplementary information to the
user.

Outputs
1. Recognized Currency: Denomination identified by the system.
2. Additional Details: Facts or data about the recognized currency (e.g., security
features, historical info).
3. Logs: Data stored for debugging, reporting, or analytics.

43
44
E-R Diagram:

Creating an Entity-Relationship (ER) diagram for game development

involves identifying the key entities and their relationships within the
currency recognition process. Here's a simplified ER diagram example:
Entities:
Currency
Attributes: CurrencyID (Primary Key), Denomination, Country, Description,
SecurityFeatures
User
Attributes: UserID (Primary Key), Name, Email, Device
CapturedImage
Attributes: ImageID (Primary Key), Timestamp, Format, FileSize
RecognitionResult
Attributes: ResultID (Primary Key), CurrencyID (Foreign Key), ConfidenceScore,
Timestamp
Model
Attributes: ModelID (Primary Key), Version, Framework, Accuracy
Relationships:
Processes
Links User and CapturedImage entities
Cardinality: One-to-Many (One user can capture multiple images)
Attributes: None
Recognizes
Links CapturedImage and RecognitionResult entities
Cardinality: One-to-One (One captured image produces one recognition result)
Attributes: None
Identifies
Links RecognitionResult and Currency entities
Cardinality: Many-to-One (Multiple recognition results can identify the same currency)
Attributes: None
Uses
Links RecognitionResult and Model entities
Cardinality: Many-to-One (Recognition results are generated using one model)
Attributes: None
45
This ER diagram captures the relationships between key entities involved in
the game development process, including developers, teams, employees,
games, and assets. It illustrates how these entities are interconnected and
how they interact with each other during the various stages of game
development.

46
47
 UML DIAGRAM

Creating a UML (Unified Modeling Language) diagram for currency

recognition involves representing the different aspects of the development
process, including classes, relationships, and interactions. Here's a simplified
UML diagram example:

48
49
USE CASE DIAGRAM:

A use case diagram is a graphical representation that demonstrates the

interactions between actors and the system to achieve specific goals. It is a
type of Unified Modelling Language (UML) diagram that provides a high-
level view of the system's functionality by illustrating the various ways users
can interact with it.

50
 FLOW CHART

A flowchart is a graphical representation of a process, system, or

algorithm, using various symbols to depict the flow of steps and
decisions. It is commonly used to visually map out the sequence of
operations or tasks in a system or process, providing a clear and easy-
to-understand outline of the workflow.

51
 BLOCK DIAGRAM

A block diagram is a visual representation of a system, process, or

algorithm that uses blocks to represent components or functions and
lines to indicate relationships or flow between them. It provides a
simplified overview of the structure and operation of a system by
breaking it down into its key components, without delving into the
intricate details.

52
Chapter:- 04

53
 System Implementation:

import os

import shutil

import random

================================================================

=============

# 1. Data Preprocessing: Organizing Dataset into Train, Val, and Test Folders

================================================================

=============

# Define paths

dataset_dir =

r"C:\Users\dwive\Desktop\python_project\.ipynb_checkpoints\currency_model.h5\Train"

output_dir =

r"C:\Users\dwive\Desktop\python_project\.ipynb_checkpoints\currency_model.h5\Test"

# Get class labels (subfolders in the dataset directory)

classes = os.listdir(dataset_dir)

# Create train, val, test folders for each class

54
for split in ['train', 'val', 'test']:

for cls in classes:

os.makedirs(os.path.join(output_dir, split, cls), exist_ok=True)

# Split the data into train, validation, and test sets

train_ratio = 0.7

val_ratio = 0.15

test_ratio = 0.15

for cls in classes:

class_dir = os.path.join(dataset_dir, cls)

images = os.listdir(class_dir)

random.shuffle(images)

# Calculate how many images will go into each split

train_count = int(len(images) * train_ratio)

val_count = int(len(images) * val_ratio)

# Copy images into the appropriate directories

for i, img in enumerate(images):

if i < train_count:

split = 'train'

elif i < train_count + val_count:

split = 'val'

else:
55
 split = 'test'

shutil.copy(os.path.join(class_dir, img), os.path.join(output_dir, split, cls, img))

================================================================

=============

# 2. Model Training: Defining the Model, Data Augmentation, and Training

================================================================

=============

import tensorflow as tf

from tensorflow.keras import layers, models

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Paths

train_dir = os.path.join(output_dir, 'train')

val_dir = os.path.join(output_dir, 'val')

# Image dimensions

img_height = 128

img_width = 128

batch_size = 32

# Data augmentation for training set

56
train_datagen = ImageDataGenerator(

rescale=1.0/255,

rotation_range=30,

width_shift_range=0.2,

height_shift_range=0.2,

shear_range=0.2,

zoom_range=0.2,

horizontal_flip=True,

fill_mode='nearest'

# No augmentation for validation set, only rescaling

val_datagen = ImageDataGenerator(rescale=1.0/255)

# Data generators for loading and augmenting images from the directories

train_generator = train_datagen.flow_from_directory(

train_dir,

target_size=(img_height, img_width),

batch_size=batch_size,

class_mode='categorical'

val_generator = val_datagen.flow_from_directory(

val_dir,

target_size=(img_height, img_width),
57
 batch_size=batch_size,

class_mode='categorical'

# Build the CNN model

model = models.Sequential([

layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)),

layers.MaxPooling2D((2, 2)),

layers.Conv2D(64, (3, 3), activation='relu'),

layers.MaxPooling2D((2, 2)),

layers.Conv2D(128, (3, 3), activation='relu'),

layers.MaxPooling2D((2, 2)),

layers.Flatten(),

layers.Dense(128, activation='relu'),

layers.Dropout(0.5),

layers.Dense(len(classes), activation='softmax')

])

# Compile the model

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model

history = model.fit(

train_generator,

epochs=25,
58
 validation_data=val_generator

# Save the trained model

model.save('currency_123.h5')

================================================================
59
=============

# 3. Image Prediction: Predicting a Single Image's Class

================================================================

=============

from tensorflow.keras.preprocessing.image import load_img, img_to_array

from tensorflow.keras.models import load_model

import numpy as np

# Load the saved model

model = load_model('currency_123.h5')

# Function to predict the class of a single image

def predict_single_image(image_path):

image = load_img(image_path, target_size=(img_height, img_width))

image_array = img_to_array(image) / 255.0 # Normalize the image

image_array = np.expand_dims(image_array, axis=0) # Add batch dimension

prediction = model.predict(image_array)

predicted_class = classes[np.argmax(prediction)] # Get predicted class

confidence = np.max(prediction) # Get prediction confidence

return predicted_class, confidence

60
# Test the function with a sample image

test_image_path =

"C:/Users/dwive/Desktop/python_project/currency_model.h5/Train/2Hundrednote/6a9d41

7e-9303-4e78-9f28-5c8c23bf54f3.jpg"

predicted_class, confidence = predict_single_image(test_image_path)

print(f"Predicted class: {predicted_class} with confidence: {confidence:.2f}")

================================================================

=============

# 4. Model Evaluation: Evaluating the Model on Test Data

================================================================

=============

from tensorflow.keras.models import load_model

from tensorflow.keras.preprocessing.image import ImageDataGenerator

import os

# Define test directory path

test_dir = os.path.join(output_dir, 'test')

# Load the saved model

model = load_model('currency_123.h5')
61
# Data generator for the test set (no augmentation, just rescaling)

test_datagen = ImageDataGenerator(rescale=1.0/255)

test_generator = test_datagen.flow_from_directory(

test_dir,

target_size=(img_height, img_width),

batch_size=batch_size,

class_mode='categorical',

shuffle=False # Ensure the order is not shuffled for evaluation

# Evaluate the model on the test set

test_loss, test_accuracy = model.evaluate(test_generator)

print(f"Test Accuracy: {test_accuracy:.2f}")

print(f"Test Loss: {test_loss:.2f}")

================================================================

=============

# 5. Confusion Matrix: Displaying Confusion Matrix for Model Evaluation

================================================================

=============
62
import numpy as np

import matplotlib.pyplot as plt

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

from tensorflow.keras.models import load_model

from tensorflow.keras.preprocessing.image import ImageDataGenerator

import os

# Define test directory path

test_dir = os.path.join(output_dir, 'test')

model = load_model('currency_123.h5')

# Data generator for the test set

test_datagen = ImageDataGenerator(rescale=1.0/255)

test_generator = test_datagen.flow_from_directory(

test_dir,

target_size=(img_height, img_width),

batch_size=batch_size,

class_mode='categorical',

shuffle=False # Shuffle is False to match the order of predictions

# Get true labels and predictions

y_true = test_generator.classes
63
y_pred_probs = model.predict(test_generator) # Predicted probabilities

y_pred = np.argmax(y_pred_probs, axis=1) # Get predicted class indices

# Class labels

class_labels = list(test_generator.class_indices.keys())

# Compute confusion matrix

cm = confusion_matrix(y_true, y_pred)

# Plot confusion matrix

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_labels)

disp.plot(cmap=plt.cm.Blues, xticks_rotation=45)

plt.title("Confusion Matrix")

plt.show()

64
Chapter:- 05

65
 TESTING
Testing in currency recognition is a critical process to ensure the quality,
functionality, and overall player experience of recognition. There are several
types of testing involved in this process:

1. Functionality Testing:
• Purpose: Ensure that all components of your currency recognition system work as
expected.
• What to Test:
o Verify that the model accurately recognizes various Indian currency notes.
o Test the user interface (if applicable) for proper functioning (e.g., uploading an
image, displaying predictions).
o Ensure that the system provides feedback, such as showing the predicted class
and confidence.
2. Compatibility Testing:
• Purpose: Test if the system works across different environments and devices.
• What to Test:
o Ensure the application works on different platforms, such as Windows, macOS,
or Linux.
o If you are using a camera for real-time recognition, verify compatibility with
different camera models.
3. Performance Testing:
• Purpose: Test how the system performs under various conditions, ensuring smooth
operation.
• What to Test:
o Measure the time it takes for the system to recognize the currency note and
output the prediction.
o Test the frame rate if you are using real-time camera input
4. Stress Testing:
• Purpose: Evaluate how the system performs under high usage or extreme conditions.
• What to Test:
o Test the system with a large batch of images (e.g., 100+ images in a row) to see
how it handles the load.
o Try feeding images with low resolution or corrupted files to see how the
system reacts.
5. Localization Testing:
• Purpose: Test if the system works with different language settings or regional
formats.
• What to Test:

66
 o Ensure that your currency recognition system is robust for different types of
input images (i.e., images with various lighting or orientations) typical for
Indian currency.
o Test if the model works with images of currency from different regions (if your
dataset includes different series of notes).
6. Usability Testing:
• Purpose: Ensure that users can interact with the system effectively and without
confusion.
• What to Test:
o Test the user interface (UI), if applicable, to ensure it’s intuitive and easy to use
for non-technical users.
o Get feedback on how easy it is to use the system for currency recognition, such
as how quickly a user can get a result.
7. Regression Testing:
• Purpose: Ensure that new changes to the system don’t introduce new bugs.
• What to Test:
o After modifying the model or adding features (e.g., real-time recognition),
ensure that the core functionality of predicting currency types is still working.
o Retest any previously fixed bugs to confirm they don't resurface.
8. Security Testing:
• Purpose: Ensure that the system is secure and resistant to external threats.
• What to Test:
o If the system interacts with a database or online API (for example, storing user
data), check for data protection and ensure sensitive data is encrypted.
o Test for vulnerabilities that could allow attackers to manipulate input images or
exploit the system.
9. Compliance Testing:
• Purpose: Ensure the system complies with any industry standards or legal
requirements.
• What to Test:
o Ensure the system does not inadvertently violate any copyright or trademark
laws when processing currency images.
o If applicable, make sure the system adheres to data protection regulations (e.g.,
GDPR) if any user data is involved.
10. Beta Testing:
• Purpose: Gather feedback from real users before the official launch.
• What to Test:
o Release a pre-launch version of the currency recognition system to a small
group of users.
o Collect feedback on its accuracy, speed, and overall user experience to identify
any remaining bugs or improvements.
67
 SYSTEM INPUT AND OUTPUT
SCREENSHOT

INPUT 1 :-

OUTPUT 1:-

68
INPUT 2:-

OUTPUT 2:-

69
INPUT 3:-

OUTPUT 3:-

70
INPUT 4:-

OUTPUT 4:-

71
72
73
74
75
76
 LIMITATION AND SCOPE OF
PROJECT
Limitation:

Certainly, game development projects face various limitations that can

impact their scope, execution, and success. Here are some common
limitations specific to game development:

Budget Constraints:

Limited financial resources can restrict the scope of the game, affecting the
quality of graphics, audio, gameplay features, and marketing efforts.
Developers must carefully allocate funds to prioritize essential aspects of
the project.

Time Constraints:

Game development timelines are often constrained by release schedules,

market demand, or external factors like seasonal events. Limited time can
lead to rushed development, compromises in quality, and the inability to
implement all desired features.

Technical Limitations:

Game development is subject to technical constraints imposed by hardware

capabilities, software tools, game engines, and platform requirements.
These limitations can affect performance, graphical fidelity, multiplayer
capabilities, and the overall complexity of the game.

77
Team Size and Expertise:
The size and expertise of the development team impact the project's
capabilities and efficiency. Smaller teams may struggle to tackle ambitious
projects or implement advanced features, while larger teams may face
coordination challenges and communication overhead.

Scope Creep:

Scope creep occurs when the project scope expands beyond its original
boundaries, leading to increased development time, costs, and complexity.
Managing scope creep is essential to prevent project delays and maintain
focus on core objectives.

Platform Limitations:

Games targeting specific platforms (e.g., PC, consoles, mobile devices)

must adhere to platform-specific guidelines, technical specifications, and
performance requirements. Porting the game to multiple platforms may
require additional resources and optimization efforts.

Market Competition:

The gaming industry is highly competitive, with thousands of games

released each year across various platforms. Standing out in the market
requires innovative gameplay, engaging content, effective marketing
strategies, and a solid understanding of player preferences.

Resource Constraints:

Game development requires a wide range of resources, including human

capital, technology, software licenses,

78
 FUTURE SCOPE

The Currency Recognition System project has significant potential for future
development and enhancement. As the field of deep learning and computer vision
continues to evolve, several advancements can be integrated into this system to improve its
functionality and expand its applications. Here are some key areas of future growth and
opportunity:

1. Enhanced Model Accuracy

Future improvements in the model’s accuracy can be achieved by expanding the dataset,
using more diverse images, and leveraging advanced deep learning techniques like transfer
learning or fine-tuning pre-trained models. This would improve recognition rates,
particularly in challenging conditions such as poor lighting or partially obscured currency
notes.

2. Integration with Mobile Devices

With the growing use of smartphones, the Currency Recognition System can be integrated
into mobile applications, allowing users to recognize currency using their phone's camera.
This would make the system more accessible and practical for everyday use, especially in
banking and retail environments.

3. Real-Time Counterfeit Detection

An advanced feature that could be added is counterfeit detection. By analyzing security
features embedded in currency notes, the system could identify counterfeit bills, enhancing
its application in security and financial sectors.

4. Multi-Currency Support
The system could be expanded to support recognition of multiple currencies, enabling its
use in global financial markets and retail environments. By training the model with
datasets from various countries, it could detect and classify foreign currencies in addition
to Indian currency.

5. Edge Computing for Faster Inference

For real-time applications, deploying the model on edge devices such as Raspberry Pi or
embedded systems would enable faster inference with lower latency, making the system
more practical for use in kiosks, ATMs, and point-of-sale terminals.

79
6. Integration with Financial Systems
The Currency Recognition System could be integrated with digital payment platforms or
banking applications, enabling automatic currency verification and transaction validation.
This could improve the efficiency of cash transactions and reduce human error.

7. Cross-Platform Compatibility
Developing the system to be cross-platform compatible would allow it to function
seamlessly on different devices such as PCs, mobile phones, and embedded systems,
enhancing its usability across various industries and platforms.

8. Augmented Reality (AR) Integration

Integrating augmented reality into the system could provide a more interactive and
intuitive user interface. Users could visualize the recognized currency with additional
information about the denomination and authenticity.

9. Blockchain for Secure Transactions

Blockchain technology could be used to store verified currency recognition data securely.
This could be useful in scenarios like currency verification in banking systems or digital
currency transactions, where trust and security are crucial.

10. Increased Focus on Accessibility

Future versions of the system could focus on making the recognition process more
inclusive, with features like voice feedback or support for visually impaired users,
ensuring broader accessibility.

These future enhancements could elevate the Currency Recognition System from a simple
tool to a robust, multifunctional application with broad usage in various sectors, including
banking, security, retail, and international finance.
4o mini

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 CONCLUSION

In conclusion, the Currency Recognition System project successfully

demonstrates the application of deep learning and computer vision
techniques to recognize and classify Indian currency in real-time. By
utilizing frameworks such as TensorFlow and PyTorch, and employing
data augmentation and image preprocessing methods, the system
achieves efficient and accurate currency recognition.
Through the development of a model that can classify currency notes
with high confidence, this project highlights the potential of machine
learning in real-world applications, especially in areas such as finance
and security. The model’s ability to adapt to various lighting and
environmental conditions ensures its robustness for practical use.
As the project progresses, further improvements can be made in terms
of expanding the dataset, optimizing the model for faster inference, and
integrating additional features such as currency denomination detection
and counterfeit identification. The success of this system demonstrates
the power of deep learning in solving complex problems and its
potential for future expansion in various domains.

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 REFERENCES

Deep Learning Frameworks

• TensorFlow: Official site for training and
deploying models.
• PyTorch: Deep learning framework with
flexibility.
Image Processing & Computer Vision
• PyImageSearch: Tutorials on computer vision
with TensorFlow.
• OpenCV: Tools for real-time image processing
and camera integration.
Machine Learning for Object Recognition
• Kaggle: Datasets and models for object
recognition.
• Towards Data Science: Articles on deep learning
and computer vision.
Currency Recognition
• Kaggle Currency Detection: Currency
recognition datasets and models.
• ResearchGate: Research papers on currency
recognition using deep learning.
Real-Time Object Detection
• COCO Dataset: Object detection and
classification dataset.
Deployment Tools
• Flask: Python framework for web deployment.
• Streamlit: Tool for building ML applications with
an easy interface.
Project Management
• GitHub: Version control for project management.

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