How can you check whether AI reads your invoices correctly?
AI can extract information from invoices and receipts, including some handwriting, but accuracy varies across documents and fields. Test representative examples, measure important fields separately and keep the original beside the proposed interpretation. Review uncertain amounts, identities and dates before they affect payments, balances or other important business records.
Keep these in mind
- Reading text correctly and recording the right business meaning are separate tests.
- Evaluate totals, dates and supplier identity separately.
- An extracted payment amount is not proof that money settled.
A readable image can still create a wrong record
A handwritten “1,800” can resemble “7,800.” A printed total can be clear while the handwritten discount below it is missed.
An invoice dated 04/05 can be ambiguous without a date convention. Recognizing characters is only the first part of the job.
Optical character recognition, or OCR, converts an image into text. A vision model can also interpret layout and surrounding content.
Check what each number represents. It could be a quantity, unit price, subtotal, advance or final amount due.
Measure the fields you actually use
Google Document AI describes precision and recall measured against labeled test documents. Precision asks how many proposed extractions were correct; recall asks how many required values were found. A single headline percentage hides which errors matter to your business.
Create an answer sheet before judging the system. For an illustrative trial of 50 receipts, record the correct supplier, date, total and currency yourself.
Keep difficult examples in the test: folds, faded ink, mixed scripts, crossed-out lines and phone photographs. Do not test only clean documents that already resemble the demonstration.
Use a field review matrix
These sample rules provide a starting point for review. Set your actual approval rules with the person responsible for the records.
| Field | Possible mistake | Review action |
|---|---|---|
| Supplier | Similar trading names | Match an existing supplier or ask |
| Total | Subtotal mistaken for final total | Compare line totals, discount and final amount |
| Date | Day and month reversed | Confirm the business date convention |
| Payment | Advance read as settled invoice | Check bank or accounting evidence separately |
Keep uncertainty visible
Some tools provide a confidence score. Treat it as a routing signal, not a promise that a value is true. A high-scoring extraction can still be attached to the wrong customer.
A low-scoring value may be correct but difficult to read. Review rules should include business context as well as model confidence.
Microsoft’s Document Intelligence transparency guidance discusses system limitations and human oversight. For your workflow, display the original next to the proposed fields. A reviewer should be able to zoom into the exact amount instead of trusting a polished summary.
A useful capture procedure
Photograph the whole document in even light. Keep all edges visible and avoid compression that erases small digits.
Preserve both the uploaded file and the reviewed interpretation. If two pages form one invoice, attach both before deciding the final amount.
For a fictional repair shop, an invoice says ₹8,400, with ₹2,000 written beneath “advance.” The useful result is not simply “payment ₹8,400.” Separate the ₹8,400 invoice from the ₹2,000 advance mentioned.
Then confirm whether the advance was received. The reviewer can then record the accepted interpretation.
- Select a representative sample and label the correct fields.
- Score each field type and list the common failures.
- Define which values always require confirmation.
- Track corrections after release and add them to the test set.
Make documents useful for the next person
Keep each document connected to the customer, supplier or job it concerns. Important extracted changes should be checked against the source before they affect another person’s work. Financial confirmation belongs with the appropriate accounting and bank evidence.
A receipt can support a follow-up while details remain unresolved. Confirm what the amount represents and whether it was received.
The best success measure is the number of correct, usable records produced with less effort. Counting uploaded documents alone rewards volume. Counting accepted fields, corrections and unresolved items reveals whether the workflow is helping the people who depend on it.
Common questions
How should I test invoice extraction quality?
Use documents that represent the real work, including handwriting, faded images and mixed layouts. Prepare the correct values first, then score supplier, date, currency and total separately. Review the errors that could affect a payment or customer record.
Why retain a receipt after its fields are extracted?
The original helps explain crossed-out amounts, missing pages and later corrections that extracted text may omit. Follow the business’s retention requirements and connect it to the accepted interpretation. An authorized colleague can then check the evidence.
Sources & further reading
Primary references for the facts and frameworks discussed. Worked examples and recommendations are Daltn’s own analysis.
- Evaluate performance: Document AI ↗
Explains field evaluation, precision, recall and confidence thresholds.
- Transparency note for Azure AI Document Intelligence ↗
Explains document-processing limitations and oversight considerations.
Published by the team building Daltn. Comparisons are first-party analysis, not independent reviews. Our editorial approach.