Document understanding

Multimodal document intelligence

Modern document models read text, page layout, and visual structure together, which is how invoices, statements, and forms carry meaning that plain text extraction loses.

Operational use
Extracting structured fields from invoices, bank statements, payroll forms, and scanned documents where position and layout carry meaning.
When not to use it
When documents are uniform and digital-native, simpler deterministic parsing is cheaper, faster, and easier to audit.
How it is validated
Field-level accuracy on a labeled sample of your real documents; control totals that must tie; a regression set of known-hard documents.
Limitations
OCR errors propagate into downstream logic. OCR-free approaches are alternatives with their own failure modes, not universal replacements. Production systems still need validation rules, confidence handling, and human review.

A financial document is not a string of text. An amount means something different in the header, the line items, and the total row, and humans read that structure without noticing. Layout-aware models such as LayoutLMv3 learn text and position together; OCR-free approaches such as Donut read the page image directly.

In production, Eigenvalue’s interest is narrower than the research frontier: which combination of OCR, layout modeling, and plain rules gives the highest field accuracy on the documents this business actually receives, and how uncertain extractions are caught before they enter a system of record. Every extraction carries confidence; everything below threshold goes to a person with the source page attached.

Where it appears in our work

Primary sources

Last reviewed 2026-08-02. Sources link to primary or official pages; verify current versions before relying on them.