Work

AI Expense Tracker

Year: 2025

Type: article

Abstract: Dealing with untracked expenses and poor financial visibility often leads to overspending and ineffective money management. This study introduces AI Expense Tracker, an automated expense monitoring system developed using machine learning, OCR, and intelligent data categorisation techniques. To improve data clarity, the proposed method preprocesses receipt images using grayscale conversion, noise reduction, thresholding, and text enhancement. Key expense information such as date, vendor, amount, and category is accurately extracted using OCR combined with rule-based validation. Extracted expenses are highlighted and organised in a structured dashboard for easy visual interpretation, and all processed data is securely stored for future analysis. A user-friendly interface built using Flask enables smooth receipt uploading, real-time tracking, and visualisation of spending patterns. The system supports smart financial planning and automated budget management by providing a scalable, efficient, and cost-effective solution for real-time personal expense monitoring.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17984434

Authors: Keerthana. M, Daruniga M V., Joy Samuvel E, Arthanari S.

Language: English

Topic: Handwritten Text Recognition Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7116395579

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