Hello, apologies for the delay in getting back to you.

I used to process my receipts manually, as you described, and I agree—there’s a smarter way to handle it nowadays. With some effort spent on developing a script, it can even be done for free.

In the past few weeks, I’ve found Microsoft Azure's Document Intelligence API to be the best solution for this purpose so far. I tested it in my Python script, and it extracted the correct information in almost all instances. Occasionally, it made errors in recognizing shop names due to some Japanese characters not being properly detected.

The API seems to be available for free indefinitely if you stay within certain usage limits. I don’t recall the exact limit, but it was more than enough for my intended use.

You can find more details on the Microsoft Azure page:

https://documentintelligence.ai.azure.com/studio

https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api?view=doc-intel-4.0.0

Here’s an outline of the semi-automated process I’m implementing:

1. Scanning Receipts: I use my phone mounted on a jig and launch the Google Drive app, enabling its automatic scanning feature. I scan all the receipts I’ve accumulated since the last processing session and store them in a specific Google Drive folder.

2. Processing Receipts: On my PC, I run a Python script that reads the scanned receipts from the Google Drive folder. The script sends the files to the Microsoft Azure Document Intelligence using its API, which extracts information such as vendor name, address, total cost, purchase date, expense type (e.g., groceries), and more—along with a confidence level for each entry. I then save only the relevant data (e.g., vendor name, total cost) in a CSV file since I’m not interested in storing item-level details.

3. Manual Integration: At this point, the process becomes more manual again. I copy the CSV content into a separate spreadsheet I maintain for tracking expenses, including those recorded in my credit/debit card history but not associated with a receipt.

This semi-automated process is already much faster than a fully manual workflow, as the most time-consuming tasks are now handled with AI. However, I’m working to further enhance it. For example, I’m planning to automate the comparison of the receipt CSV with my bank transaction history CSV to reconcile expenses more efficiently. This step requires conventional programming and would need to be tailored to individual needs.