Glossary · Austria and Germany
Document AI and OCR
OCR recognises characters in scanned documents or images. Document AI may additionally structure content and identify invoice numbers, amounts or dates. Extraction does not prove document authenticity or establish a legally valid claim.
Distinguish the concepts
Readable text and correct field mapping are different. A number may be transcribed accurately but assigned to the wrong invoice. High technical confidence does not remove the need to verify critical details. Keep the original, extracted values and human corrections distinct. Changes should remain traceable instead of silently overwriting the only evidence.
Jurisdiction and scope
The distinction applies to claims in both Austria and Germany. Personal invoice and contact data also require appropriate data protection. The Microsoft documentation explains one extraction product; it does not establish that incaseof.law uses that supplier. This glossary does not promise a particular recognition rate.
Illustrative business example
A scan turns EUR 1,080 into EUR 7,080. Before handover, an employee reconciles amount, currency, invoice number and debtor with the original and accounts. A credit note in the same PDF is recorded separately. The example illustrates why automated import needs a controlled verification step.
Common questions
Is OCR itself artificial intelligence?
OCR names the text-recognition task. Implementations differ, while document analysis often adds classification and field mapping.
Which fields need particular care?
Identity, amount, currency, dates, payment terms and bank details can directly affect a claim or communication.
Can the original be discarded?
Successful extraction does not establish that. Consider evidential value and retention requirements separately.
Related topics
Sources and basis
Content reviewed on 23 September 2026. Procedural information and our contractual terms are separate foundations. The individual case remains decisive.
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