01 / PROJECTS
Reading receipts, checking the output
A receipt scanner that turns a photo into itemized purchase data, then checks the model’s output against the scanned text. Everything runs locally.
Checking the extraction against the receipt.
The validation work began with an unreadable receipt: the model returned 15 purchases drawn from examples in its prompt. I used that case to develop checks that caught all 15 unsupported items in the example.
Low-confidence scans are rejected before the model runs, and empty receipt sections are skipped. For each item the model returns, the grounding filter looks for a matching word in the scanned text, allowing one character of OCR error in words at least four characters long.
The extracted amounts are also checked against the printed total. A mismatch is flagged for review rather than silently accepted.
Scope and context
The checks flag entries for review by comparing them with the scan. Photo quality and model output still affect the result, and a text match does not establish that every item or price is correct. The 15-item example is a documented test case rather than a general accuracy benchmark.