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https://hdl.handle.net/10356/183816
Title: | Artificial intelligence bill splitting web application with OCR technology | Authors: | Ng, Jaren Shing Yu | Keywords: | Computer and Information Science | Issue Date: | 2025 | Publisher: | Nanyang Technological University | Source: | Ng, J. S. Y. (2025). Artificial intelligence bill splitting web application with OCR technology. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/183816 | Abstract: | Rising global living costs have made equitable expense-sharing among friends and family increasingly important. Existing bill-splitting applications often assume equal cost distribution, neglecting scenarios where consumption varies among group members. This oversight commonly leads to errors, frustrations, and disputes in financial transactions. To address these limitations, this project proposes an AI-enabled bill-splitting web application designed for more accurate, personalized, and efficient cost division. By integrating Optical Character Recognition (OCR) for receipt scanning, text recognition, and customizable cost allocation using Artificial Intelligence (AI), the website application (Web App) aims to significantly reduce manual effort and potential errors. This approach provides a comprehensive solution that accommodates different consumption levels, goods and service taxes (GST), and service charges, ensuring a more precise and equitable experience for users. The result is a convenient, user-friendly web app that lowers the risk of financial disputes, efficiency of bill-splitting, meeting the demands of modern users and filling a crucial gap in the current market. | URI: | https://hdl.handle.net/10356/183816 | Schools: | College of Computing and Data Science | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | CCDS Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
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Jaren_Ng_Shing_Yu_AI_Bill_Splitting_Web_App_April_2025.pdf Restricted Access | 4.12 MB | Adobe PDF | View/Open |
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