# OpenText Intelligent Capture vs Captiva OCR

> Understand OpenText Intelligent Capture, legacy Captiva OCR search intent, and how to evaluate capture modernization with sample documents.

Searches for OpenText Intelligent Capture and Captiva often come from teams trying to understand whether an older capture workflow is the same thing as a newer intelligent document processing workflow.

The short answer: treat this as a modernization question. Do not compare names alone. Compare the actual capture process, OCR quality, extraction workflow, integrations, review queues, and export requirements.

## What capture software is supposed to do

Document capture tools turn incoming paper, scans, PDFs, faxes, and files into usable business data.

A capture workflow usually includes:

- Intake.
- Image cleanup.
- OCR.
- Layout recognition.
- Barcode or separator detection.
- Document classification.
- Field extraction.
- Human validation.
- Export to content management, ERP, workflow, or analytics systems.

Modern "intelligent capture" adds more AI-assisted classification and extraction, but the business problem is the same: get reliable data out of messy documents.

## Why Captiva comes up

Captiva is an older name many enterprise teams still know from legacy capture and OCR deployments.

When someone searches for Captiva OCR, they may be asking:

- Is Captiva still the product name?
- What is the modern OpenText equivalent?
- Should we migrate or keep the current workflow?
- Which integrations will break?
- Can newer AI extraction replace templates?
- What should we test before a modernization project?

Before publishing a vendor-specific claim, verify the current OpenText product naming and support status. Product names change.

## Compare by workflow, not by name

Use this checklist:

| Area | What to check |
| --- | --- |
| Intake | scanners, email, watched folders, upload portals, fax |
| OCR | language support, image cleanup, handwriting, searchable PDF |
| Classification | document type detection, separation, routing |
| Extraction | fields, tables, line items, confidence scores |
| Human review | validation queues, exception handling, audit trail |
| Integrations | Documentum, SAP, Microsoft, ERP, workflow, APIs |
| Governance | retention, access, logs, encryption, region |
| Migration | templates, scripts, connectors, training data, exports |

## Inventory the current capture process

Start with the current system, not the replacement.

Document:

- Which document types enter the system.
- Where they come from.
- Monthly volume.
- Error rate.
- Human review time.
- Downstream systems.
- Existing templates and rules.
- Export formats.
- Compliance requirements.

If you cannot describe the current process, you cannot safely modernize it.

## Build a sample test set

Create a folder:

```text
capture-modernization-test/
README.md
samples/
invoice-clean.pdf
invoice-poor-scan.pdf
claim-form-handwritten.pdf
multipage-contract.pdf
fax-sample.tiff
expected-fields.csv
opentext-notes.md
alternatives-notes.md
scorecard.csv
```

Use real structure, but safe data. Remove customer secrets and regulated personal data unless the test environment is approved for it.

## Score extraction, not demo quality

For each sample, score:

- OCR text quality.
- Page separation.
- Document type classification.
- Field extraction.
- Table and line-item extraction.
- Confidence scores.
- Human correction workflow.
- Export quality.
- Processing speed.
- Failure mode clarity.

The key metric is not whether the tool looks intelligent. It is how much human cleanup remains.

## Compare modernization paths

Possible paths:

- Keep the current capture workflow and improve templates.
- Upgrade within OpenText.
- Add AI extraction around the existing workflow.
- Move some document types to Google Document AI or Azure AI Document Intelligence.
- Replace the whole capture workflow.

For each path, estimate:

- Migration effort.
- Integration risk.
- Reviewer training.
- License and infrastructure cost.
- Compliance review.
- Expected reduction in manual work.

## Sources checked

- OpenText capture product page: https://www.opentext.com/products/capture
- OpenText product navigation and capture/intelligent document processing references: https://www.opentext.com/products/capture
- Google Document AI overview for alternative comparison context: https://docs.cloud.google.com/document-ai/docs/overview
- Azure AI Document Intelligence overview for alternative comparison context: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview

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