GUIDE — TECHNOLOGY
OCR vs vision-language models: an honest comparison
Short answer: traditional OCR excels at reading characters from fixed layouts; vision-language models excel at understanding documents they have never seen. Production document processing usually needs the judgment of the second with the rigor that neither provides out of the box.
What traditional OCR does well — and where it breaks
Template OCR and zonal extraction are fast, cheap and predictable when documents are standardized: same layout, same fields, same positions. They break the moment layouts vary — a new supplier, a redesigned form, a scanned photo at an angle — because they read positions, not meaning.
- Strengths: speed, cost per page, deterministic behavior on fixed layouts
- Limits: template maintenance, no semantics, brittle on layout variation
What vision-language models change
VLMs read a document the way a person does: layout, text and context together. They extract fields from documents they have never seen, adapt to variation without templates, and can infer what fields matter for an unfamiliar document type. The cost: higher per-page compute, non-determinism, and the risk of confident-sounding errors if outputs are not validated.
- Strengths: zero-template generalization, semantic understanding, multilingual
- Limits: cost per page, variance between runs, hallucination without guardrails
What neither gives you alone
A raw model call is not a document pipeline. Production processing needs consistent structured output, a confidence score per field, validation rules that catch hallucinations, cross-document checks, human review routing, and an audit trail. That layer — not the model choice — is where reliability comes from.
Sygnet is that layer: VLM-based extraction wrapped in validation, confidence scoring, review workflows and audit, delivered through one API.
NEXT STEP