Development of a Student Expense Tracking System Using Optical Character Recognition

Personal financial literacy is a vital skill for university students, yet many struggle to track their daily expenses due to time constraints and low awareness. This study aims to design and develop a web-based Student Expense Tracking System using Optical Character Recognition (OCR) technology to address this issue. The system allows users to automatically extract and record spending information from receipt images, reducing manual input and improving financial awareness. The development followed the Web Development Life Cycle (WDLC) using the Waterfall model, comprising planning, design, development, and testing phases. Visual Studio Code, Python 3, and Tesseract OCR were employed in system implementation. Wireframes and mockups guided the interface design, while backend development focused on data storage and OCR integration. Functionality testing showed a 100% pass rate across ten scenarios, validating the system's performance in image processing, budget management, and spending visualization. Usability testing using the Post-Study System Usability Questionnaire (PSSUQ) with 30 participants yielded a mean score of 4.45 out of 5, indicating a high level of user satisfaction. The system scored highest on ease of use (4.6), visual design (4.7), and recommendation likelihood (4.8), confirming its intuitive interface and appeal. Slightly lower scores in user confidence (4.1) and data organization (4.2) point to opportunities for interface refinement and improved user guidance. This research concludes that OCR can effectively support financial tracking for students. Future enhancements with NLP and machine learning are recommended to automate expense categorization and improve analytical capabilities.