Extracting structured data from invoices looks straightforward until you run it at scale. Invoices arrive as PDFs, scans, and photos; they follow different layouts, languages, and fonts, and many contain tables, stamps, handwritten notes, or low-quality images. Even when the information is present, it is often split across lines, repeated in multiple places, or labeled inconsistently, which makes simple pattern matching unreliable. Moreover, we can face issues in numeric and alphanumeric fields, such as VINs and invoice numbers, which are especially error-prone because visually similar characters get swapped, for example, o and 0, w and v, 5 and s, or i and l.
The hardest part is that small errors are costly. A single misread character in an invoice number, a swapped decimal separator in a total amount, or a billing address confused with a shipping address can break downstream automation and trigger manual review. A robust solution usually combines several layers: document ingestion and preprocessing, classic OCR and PDF text extraction, rule-based parsing for predictable patterns, business validation rules such as total consistency and identifier checks, and a workflow that routes low-confidence cases to human review.