"Enhancing Personal Finance Management through OCR and Data Visualization" aims to utilize data analyses and data visualization techniques to address the gap in personal finance management tools for individuals, specifically students. Majority of the existing financial insight or management applications, such as moss, pleo, concur, and more, are business oriented. A tool designed to help business track and manage their expenses might fulfil the basic requirements of individual users but cannot provide the insight they are looking for or understand.

Financial management tools, like mentioned above, are designed for businesses often include features that function well with business practices, such as tracking invoices, managing payroll, and preparing detailed financial reports suitable for stakeholders. These applications are designed to handle complex transactions, multiple accounts, and the tax implications that are specific to businesses, which might overwhelm any individual who lack knowledge in the relevant areas, while also making the interface and data visualization difficult to understand for individual users.

For individuals, especially students, the financial landscape is vastly different. They require simpler interfaces and functionalities that focus on daily expenses, personal budgeting, savings goals, and straightforward spending analytics. To understand what the students are looking for in the insight of the expenses and saving, we need learn more through interviews or anonymous surveys. Not to mention, business tools can be too complex, expensive, and feature-rich for individual needs, leading to a steep learning curve and unnecessary complications in managing personal finances. Individuals benefit from tools that are more accessible, intuitive, and focused on personal expense tracking and visualization.

The problem is faced by all students, whether they are home students or international students, they will face financial trouble, most of the time it is because they have little to no experience in managing their expenses. Hence, I plan to tackle this problem and more specifically for students.

Firstly, I will use Optical Character Recognition (OCR), which is an important used to interpret and convert different types of documents such as scanned papers, PDFs, or images into editable and searchable text. Since, I will be using a OCR API which is available to use ranging for open-source and paid services, I will not be going in too much detail as to how it works. In brief, it works by analyzing the shapes within images, detecting patterns that correspond to letters and numbers, and matching these to a database of known characters. Although it sounds simple it is a little more complex, OCR technology is particularly useful for converting receipts into digital data, despite variations in text style and image quality.

More importantly, the project will focus more on the use of Python's data visualization libraries (such as Matplotlib, Seaborn, and Plotly) to transform data, collected using OCR from receipts into intuitive and insightful visual representations. These visualizations have not been decided yet, like mentioned above I will conduct an anonymous survey to see what students are looking for in their financial insights, and what can help them to better understand their spending habits. The focus on data visualization is crucial for explaining complex financial data, making it accessible and actionable for the average user. The plan is to create a single dashboard which will visualize the data collect from the receipts, using the above-mentioned python libraries.

This project addresses a critical need for personalized financial tools that cater to individual (students) spending behaviours and preferences. Through iterative development and user feedback cycles, including interviews and surveys, the project will refine the visualized data to match user expectations, providing a tool for personal finance m