OCR: pixels to characters
Optical character recognition does one job: it looks at an image and produces the text in it. Mature, fast, cheap — and that's the whole contract. OCR gives you a wall of text with no idea which part is the invoice number, no notion of tables, and degraded output exactly where documents get hard (handwriting, stamps, poor scans).
OCR alone is the right tool when you literally need the text — making a scanned archive searchable, for instance. It is the wrong tool when you need fields.
IDP: the workflow stack built on OCR
Intelligent document processing grew up around OCR's limitation. A classic IDP platform chains stages: classify the document type, run OCR, locate fields with templates or trained ML models, validate against business rules, route exceptions to humans, integrate downstream. Platforms like ABBYY, Rossum and Nanonets made this the enterprise standard.
IDP works, but the classic stack carries classic costs: per-document-type model training or template setup, professional services, and pricing to match. The intelligence was expensive because it had to be assembled from narrow parts.
LLM extraction: the stack, collapsed
Multi-modal large language models changed the economics. One model now reads the image directly (no separate OCR pass), understands layout and tables, identifies fields from a plain-language schema (no template training), and handles any language and handwriting it can see. The first three stages of the IDP pipeline collapsed into a single model call.
What didn't collapse is the workflow around the model: validation, human review, exports, delivery. Models are excellent and not perfect — production systems still need the machinery that catches the imperfect cases. That's the architecture DocParse ships: LLM extraction at the core, with validation rules, a review queue, exports, API, webhooks and email-in around it.
Which one do you need?
| You need to… | Use |
|---|---|
| Make scanned documents searchable | Plain OCR |
| Extract fields from varied documents | LLM extraction |
| Sort mixed documents into categories | LLM classification |
| Run enterprise AP with approval chains in one suite | Full IDP platform |
| Embed extraction inside your own product | LLM extraction via API |
Questions that cut through vendor language
Whatever a vendor calls their technology, four questions reveal the generation underneath: Does a brand-new layout need setup? (Templates/training = older stack.) Do scans and photos work without a separate OCR step? Do you charge for model training? What happens to a document the system isn't confident about?
The last one matters most in production. The honest answer for every technology generation is 'a human checks it' — so the quality of the review workflow is as important as the headline accuracy. More on that in document information extraction, explained.
Frequently asked questions
Is OCR still relevant in 2026?
Yes, for its core job: digitising text at very large scale, cheaply. For field extraction, OCR alone was never enough — it's the layer underneath, and in LLM extraction it's absorbed into the model itself.
Is LLM document extraction accurate enough for finance documents?
On most real-world documents, frontier multi-modal models extract very accurately — but production finance workflows should always pair extraction with validation rules and human review for flagged documents. Accuracy plus a safety net beats accuracy alone.
What does IDP stand for?
Intelligent Document Processing — the category of software that automates document workflows end to end: classification, extraction, validation, human review and integration.