expense-tracker

Automate expense tracking by using OCR to extract merchant, total, and date details from receipt images, auto-categorizing the data, and storing it in an SQLite database. This process eliminates manual data entry for personal or business expenses.

Receipt OCR works by processing receipt images with optical character recognition to identify key details like merchant, total, and date. The extracted text is then parsed and auto-categorized into predefined groups like Food or Transport based on keywords.

Yes, you can export tracked expenses to Excel. The expense data is initially saved in an SQLite database and can be exported to Excel format using openpyxl, allowing you to generate daily and monthly summaries with visual charts.

Yes, you need pytesseract and pillow for OCR image processing, pandas for data manipulation, and matplotlib with seaborn for visual reporting. These dependencies enable the deterministic Python scripts to add, list, filter, and summarize expenses.

The best way to categorize expenses from receipts is through auto-categorization based on keywords. The system assigns extracted transaction details to predefined categories like Food or Transport, streamlining the budgeting and tracking process.

Limitations of using OCR for receipt expense tracking include dependency on image quality for accurate text extraction. While it automates auto-categorization and SQLite storage, poorly captured receipt photos may result in incomplete or inaccurate data parsing.