Ten-Receipt OCR Review Example
Review a completed ten-record receipt archive where each source image remains attached to structured and plain-text extraction.
Scenario
The specific job behind this example
An operator needs to turn mixed receipt photos into searchable records while retaining a clear human correction step.
Representative completed values
Real fields and representative values
| File | Vendor | Address | Category | Currency |
|---|---|---|---|---|
| strong flour.jpg | STRONG FLOUR | 30 East Coast Road, Singapore | Food | SGD |
| Receipt_California.jpg | BOULEVARD | One Mission Street, San Francisco | Food | USD |
Input
What the work started with
- Ten receipt images from different merchants.
- A defined schema for vendor, address, category, JSON, and OCR text.
- A reviewer responsible for ambiguous values.
Output
What the completed workspace contains
- Ten traceable receipt records.
- Searchable merchant and category fields.
- Structured JSON and full OCR text beside each source image.
The result is complete only after these checks.
A generated field, copied record, or finished diagram is not automatically correct. These checks define the minimum acceptance boundary for this example.
Validation checklist
- There is exactly one record per source receipt.
- Currency, vendor, address, date, tax, and total are checked when present.
- Incomplete records remain visible and do not block valid receipts.
Reproduce the result
Receipt OCR Table Template
Inspect the starter structure, known limitations, product source, and setup steps before adapting this completed example to your own project.
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Ready when you are.
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