Student Expense Tracking System Using OCR
DOI:
Abstract
Nowadays student schedules are packed with their academic and curricular activities. Therefore, Students are no longer tracking their expenses because it is so hard to keep track with their expenses when they a have busy life. The aim of this research is to help students easily track their expenses by automating the process of extracting information from receipts. This research presents a student tracking expenses system using Optical Character Recognition (OCR) technology. The method that was used to develop the system was Website Development Life Cycle (WDLC). The system also uses Image Processing that implements OCR into the system. The system has been tested with a set of sample receipts, and the results show that it is able to accurately extract the relevant information with a high level of efficiency. The initial of this research involved designing the system, which was achieved through the creation of a detailed mockup and wireframe to establish a clear vision for its design. Then, it focused on developing the system, incorporating OCR technology to extract text from receipts. Thorough functional testing ensured that all system features, including user identification, image upload and OCR processing, expenditure management, budget setting, and data visualization, functioned as intended. The system offers users accurate and dependable capabilities for spending pattern analysis, budget management, and expense monitoring. Furthermore, the usability testing was conducted using the Post-Study System Usability Questionnaire (PSSUQ) from 30 students. The mean score of the System Usefulness, Information Quality and Overall Satisfaction is above 4 which indicates that it was appreciated by the students or respondents. Therefore, this system can be a valuable tool for students to manage their finances and make informed decisions about their spending.
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References
J. Bonke, Do Danish Children and Young People Receive Pocket Money?, 2013. [Online]. Available: www.rff.dk. [Accessed: Jan 2025].
K. Charntaweekhun and S. Wangsiripitak, Visual Programming Using Flowchart, 2006.
J. L. Yeo, P. S. JosephNg, K. A. Alezabi, H. C. Eaw, and K. Y. Phan, “Time Scheduling and Finance Management: University Student Survival Kit,” in Proc. IEEE Student Conf. Research and Development (SCOReD), pp. 16–21, 2020.
I. S. Bakhtiar, M. H. Jamaluddin, M. A. M. Salim, and M. N. Harun, “Student Spending Non-Academic Item – Lead to Poor Financial Management,” in Proc. Int. Conf. Student and Disable Student Development (ICoSD), vol. 407, no. 1, 2019.
R. Manju, “Trends in Spending and Money Management Practices among Students of Kerala,” Int. J. Research in Humanities and Social Studies, vol. 3, no. 10, 2016.
N. Susanti, R. Rahmayanti, R. R. Padmakusumah, and R. Susanto, “Factors Affecting Students’ Financial Literation: A Study on Widyatama University, Indonesia,” Universal J. Educational Research, vol. 7, no. 5, pp. 7–14, 2019.
M. Selvanathan, U. D. Krisnan, and W. C. Wen, “Factors Effecting Towards Personal Bankruptcy among Residents: Case Study in Klang Valley, Malaysia,” Int. J. Human Resource Studies, vol. 6, no. 3, p. 98, 2016.
L. Falahati and L. H. Paim, “Toward a Framework of Determinants of Financial Management and Financial Problems among University Students,” African J. Business Management, vol. 5, no. 22, pp. 9600–9606, 2011.
S. M. Danes and T. K. Hira, “Money Management Knowledge of College Students,” J. Student Financial Aid, vol. 17, no. 1, 1987.
R. Rikwentishe, B. Musa Pulka, and S. K. Msheliza, “The Effects of Saving and Saving Habits on Entrepreneurship Development,” Eur. J. Business and Management, vol. 7, no. 23, 2015.
S. Alwi, I. Zura, A. Hashim, and M. S. Ali, “Factors Affecting Savings Habits within Millennials in Malaysia: Case Study on Students of Taylor’s University,” 2015. [Online]. Available: www.globalbizresearch.org. [Accessed: Jan 2025].
W. Satyawan et al., “Citizen ID card detection using image processing and optical character recognition,” Journal of Physics: Conference Series, vol. 1235, no. 1, 2019.
B. Basavaprasad and M. Ravi, “A study on the importance of image processing and its applications,” International Journal of Research in Engineering and Technology, 2020.
H. Kaur and N. Sohi, “A study for applications of histogram in image enhancement,” The International Journal of Engineering and Science, vol. 6, no. 6, pp. 59–63, 2017.
N. D. S. Mohamad Ghazali and A. F. Saad, “Online student performance system integrating multidimensional data visualization and chatbot for primary school,” International Journal of Artificial Intelligence, vol. 9, no. 2, pp. 63–73, 2022.
K. Jain, T. Choudhury, and N. Kashyap, “Smart vehicle identification system using OCR,” in Proc. 3rd IEEE International Conference on Advances in Computing, Communications and Informatics, 2017.
A. Abdulrahman and S. Varol, “A review of image segmentation using MATLAB environment,” in Proc. 8th International Symposium on Digital Forensics and Security (ISDFS), 2020.
S. Rani, S. Jindal, and B. Kaur, “A brief review on image restoration techniques,” International Journal of Computer Applications, vol. 150, no. 12, pp. 30–33, 2016.
S. I. Ao et al., International MultiConference of Engineers and Computer Scientists (IMECS 2014), International Association of Engineers, Hong Kong, 2014.
K. Flora, X. Wang, and S. V. Chande, “An investigation into mobile application development processes: Challenges and best practices,” International Journal of Modern Education and Computer Science, vol. 6, no. 6, pp. 1–9, 2014.
E. D. Liddy, Natural Language Processing. Syracuse University, 2001.
R. Maini and H. Aggarwal, A Comprehensive Review of Image Enhancement Techniques, vol. 2, 2010.
C. A. Makridis, J. D. Kelly, and G. Alterovitz, “Using machine learning to understand veterans’ receipt of loans in the paycheck protection program,” SSRN, 2020. Available: https: //ssrn.com/abstract=3725665. [Accessed: Jan 2025].
D. K. S. Neoh et al., “PicToText: Text recognition using Firebase Machine Learning Kit,” in Proc. 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS), 2021.
T. T. H. Nguyen et al., “Deep statistical analysis of OCR errors for effective post-OCR processing,” in Proc. ACM/IEEE Joint Conference on Digital Libraries, pp. 29–38, 2019.
A. R. Putra, S. H. Wijoyo, and Y. T. Mursityo, “Evaluasi usability dan perbaikan user interface pada aplikasi KRL Access menggunakan metode HCD dan PSSUQ,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 4, no. 1, 2020.
N. Sawarkar, P. Yenagandula, D. Shetye, and S. Agrawal, “Expense Manager: An expense tracking application using image processing,” International Research Journal of Engineering and Technology, 2022.
R. Smith, “Limits on the application of frequency-based language models to OCR,” in Proc. International Conference on Document Analysis and Recognition (ICDAR), pp. 538–542, 2011.
I.-Y. Song, M. Evans, and U. E. K. Park, “A comparative analysis of entity-relationship diagrams,” Journal of Computer and Software Engineering, vol. 3, no. 4, 1995.
S. Weil, “UB-Mannheim/tesseract,” GitHub, 2023. Available: https: //github.com/UB-Mannheim/tesseract. [Accessed: Jan 2025].
A. F., “Online convenience store system using multidimensional data visualization,” International Journal of Education, Science, Technology, and Engineering (IJESTE), vol. 6, no. 1, pp. 25–34, 2023.
