Keep the original document, distinguish extracted and verified fields, validate arithmetic and references, and approve the ERP handoff separately from extraction.
1. Define the invoice record before choosing OCR
List the fields your process actually needs: supplier reference, invoice number, invoice date, currency, purchase-order reference, line items, tax, and totals. Define the expected format and which fields must match an existing business record.
Keep the original file and an intake identifier. Record where the invoice came from and whether the same document has already been submitted. OCR reads text from an image; a model or extraction service may help identify the fields. Neither step establishes that the supplier, bank details, or amount should be accepted.
2. Show evidence alongside extracted values
Separate the raw extracted value from the normalized value the application proposes to use. A date such as 03/04 can be ambiguous. An unfamiliar currency symbol or a poorly scanned decimal point should become a review issue, not a confident guess.
Where supported, let the reviewer inspect the page or region associated with a field. Preserve missing values as missing. A low-quality scan, multi-page invoice, credit note, or handwritten correction may need a different handling path from a clean digital PDF. Include these formats in the evaluation collection.
3. Use business checks after extraction
Validate required fields, date formats, supplier and purchase-order references, and arithmetic using application code. Money should use a suitable decimal representation and currency precision. Decide which rounding and tax rules apply to your accounting setup.
The following standard-library Python example only checks already normalized sample amounts. It does not perform OCR, calculate jurisdiction-specific tax, or approve a payment. Passing this check is one validation result, not proof that an invoice is genuine.
from decimal import Decimal
invoice = {"subtotal": "120.00", "tax": "24.00", "total": "144.00"}
subtotal = Decimal(invoice["subtotal"])
tax = Decimal(invoice["tax"])
total = Decimal(invoice["total"])
if subtotal + tax != total:
raise ValueError("Totals need review")
print("Arithmetic matches; supplier and business checks still need review.")4. Build an exception queue a reviewer can use
Group the problems that need attention: unknown supplier, missing purchase order, conflicting totals, duplicate invoice number, or unreadable pages. Show what needs to be resolved and preserve the reviewer’s correction separately from the original extraction.
Duplicate checks need a business key, not just a filename. A supplier may resend the same invoice under another file name, and different suppliers may use the same invoice number. Agree on how supplier identity, invoice number, document fingerprint, and amounts contribute to review. Do not silently discard a document solely because a model thinks it looks similar.
5. Make the ERP handoff explicit and repeatable
Map the approved fields to the target system’s actual import or API contract. Check edition, API access, required references, and whether the operation creates a draft bill or another record. Retain the target identifier and the response from the business system.
Use an operation identifier so retries do not create a second bill. If the network times out after submission, reconcile the target state before trying again. Measure reviewer correction time and failed handoffs alongside extraction quality. Start with a limited document type and expand only after the review and reconciliation process works.
6. Frequently asked questions
Does OCR alone automate invoice entry?
No. OCR produces text. A complete workflow also needs field extraction, normalization, business validation, duplicate handling, review, and a controlled handoff to the accounting system.
Can we post invoices automatically?
That is a separate scope decision. First establish which documents and rules are supported, how exceptions are handled, and who can approve posting. An extraction pilot does not prove readiness for automatic accounting actions.
How should we measure the result?
Review field-level accuracy, missing values, correction time, duplicate handling, and successful ERP reconciliation on representative documents. A single overall accuracy number can hide errors in important fields.
Estimate time for this repeated task or Discuss an AI or automation workflow.
7. Related articles and next steps
Bring representative inputs, expected outputs, and the systems involved. Discuss your requirements with OdooMatrix.