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How to compare OCR and document AI for data extraction?

Compare OCR and document AI for data extraction by looking beyond text recognition to fields, evidence, confidence, review workflows, and system-ready records.

Compare extraction approaches

What is the difference between OCR and document AI?

OCR recognizes text in images or scanned files. Document AI goes further by identifying fields, relationships, clauses, document context, confidence, and review needs.

An illustration of data being extracted from documents

When is OCR enough?

OCR can be enough when the goal is searchable text. It is not enough when teams need reliable business fields, source evidence, workflow decisions, and downstream system updates.

an illustration of documents

How does TextMine use extraction in workflows?

TextMine focuses on governed extraction: source links, confidence, reviewer state, records, playbook rules, and workflow routing for operational use.

an illustration of vault extracting data from contracts and answering questions about them

Example comparison

OCR reads a supplier contract. Document AI identifies the renewal clause, extracts the notice period, links to the source page, and routes review if the clause conflicts with policy.

OCR output
Recognized text
Document AI output
Structured, source-backed fields
Best for OCR
Searchable scanned text
Best for document AI
Data extraction and workflow automation
Evidence need
Page, snippet, confidence, source link
Review need
Human approval for uncertainty
System need
Records and integrations
OCR output
Recognized text
Document AI output
Structured, source-backed fields
Best for OCR
Searchable scanned text
Best for document AI
Data extraction and workflow automation
Evidence need
Page, snippet, confidence, source link
Review need
Human approval for uncertainty
System need
Records and integrations

Example comparison

OCR reads a supplier contract. Document AI identifies the renewal clause, extracts the notice period, links to the source page, and routes review if the clause conflicts with policy.

OCR output
Recognized text
Document AI output
Structured, source-backed fields
Best for OCR
Searchable scanned text
Best for document AI
Data extraction and workflow automation
Evidence need
Page, snippet, confidence, source link
Review need
Human approval for uncertainty
System need
Records and integrations
OCR output
Recognized text
Document AI output
Structured, source-backed fields
Best for OCR
Searchable scanned text
Best for document AI
Data extraction and workflow automation
Evidence need
Page, snippet, confidence, source link
Review need
Human approval for uncertainty
System need
Records and integrations

Product map

Match each document job to the right TextMine surface

These guides can mention several products because enterprise document work rarely stops at extraction. Use this map to see how review, evidence, orchestration, records, rules, and system handoff fit together.

WorkbenchReview documents, compare files, ask questions, and produce source-aware outputs.VaultExtract document facts and keep every answer tied to source evidence and review state.WorkflowsRoute exceptions, approvals, evidence requests, remediation tasks, and handoffs.RecordsTurn approved findings into governed records for search, reporting, export, and sync.PlaybooksCodify policies, review criteria, templates, and acceptable positions.IntegrationsSend verified document context into agents, APIs, CRMs, ERPs, and compliance tools.

Map TextMine to your document workflow

Tell us what you are trying to review, extract, route, or report on. We will show which TextMine products fit your process, from Workbench and Vault through Workflows, Records, Playbooks, and Integrations.

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