Azure Computer Vision - OpenText eDOCS Integration and Automation

Common Integration Use Cases Between Azure Computer Vision and OpenText eDOCS

1. Automatic OCR and Filing of Scanned Legal Documents

Data flow: Azure Computer Vision ? OpenText eDOCS

Scan-based legal teams can use Azure Computer Vision to extract text from incoming paper documents, signed agreements, court filings, and correspondence. The extracted text and document metadata can then be pushed into OpenText eDOCS for matter-based filing, indexing, and version-controlled storage.

• Reduces manual data entry for paralegals and legal assistants
• Improves searchability of scanned documents inside eDOCS
• Speeds up intake of high-volume mailroom and records workflows

2. Auto-Classification of Visual Evidence and Case Attachments

Data flow: Azure Computer Vision ? OpenText eDOCS

Legal teams handling litigation, investigations, or compliance cases can use Azure Computer Vision to identify document types, detect objects, and extract visual cues from photos, screenshots, and exhibits. The results can be used to classify and route files into the correct matter folders in OpenText eDOCS.

• Supports faster organization of evidence packages
• Improves consistency in matter-centric document filing
• Helps legal teams locate relevant visual materials more quickly

3. Redaction Support for Sensitive Content Identification

Data flow: Azure Computer Vision ? OpenText eDOCS

Azure Computer Vision can detect text in images and scanned PDFs to help identify documents that may contain personally identifiable information, account numbers, signatures, or other sensitive content. OpenText eDOCS can then store the document with the appropriate security controls, retention rules, and access restrictions.

• Strengthens document review and privacy controls
• Helps legal and compliance teams prioritize redaction work
• Reduces risk of accidental disclosure in shared matters

4. Matter Intake from Client-Submitted Images and Photos

Data flow: Azure Computer Vision ? OpenText eDOCS

When clients submit photos of contracts, receipts, damage evidence, site conditions, or handwritten notes, Azure Computer Vision can extract text and identify the image content. OpenText eDOCS can then store the files in the correct matter and associate them with related case documents.

• Improves intake of mobile and email-submitted evidence
• Creates a more complete matter record
• Reduces delays caused by manual sorting of client materials

5. Enhanced Search and Discovery for Legal Archives

Data flow: Azure Computer Vision ? OpenText eDOCS

Azure Computer Vision can generate searchable text and image-derived metadata for legacy scanned archives stored in OpenText eDOCS. This makes older matter files easier to find by keyword, document content, or visual attributes.

• Unlocks value from legacy paper archives
• Improves retrieval speed for attorneys and records teams
• Supports large-scale digitization programs

6. Automated Organization of Signed Agreements and Exhibits

Data flow: Azure Computer Vision ? OpenText eDOCS

After contracts, exhibits, and supporting documents are scanned or photographed, Azure Computer Vision can detect signatures, stamps, logos, and text blocks. OpenText eDOCS can use that information to classify the file, link it to the correct matter, and store it with the final executed version.

• Helps distinguish final signed documents from drafts
• Improves document control in legal operations
• Supports audit-ready recordkeeping

7. Quality Control for Incoming Records and Docketing Materials

Data flow: Azure Computer Vision ? OpenText eDOCS

Legal operations teams can use Azure Computer Vision to verify whether scanned documents are legible, complete, and contain expected text or page content before they are committed to OpenText eDOCS. Documents that fail quality checks can be routed for re-scan or manual review.

• Reduces filing errors and incomplete records
• Improves downstream accuracy for legal review and e-discovery
• Creates a more reliable document repository

8. Matter-Centric Enrichment of Visual Content

Data flow: Bi-directional

OpenText eDOCS can provide matter context, security classification, and document ownership, while Azure Computer Vision can enrich the content with OCR text, image labels, and object detection results. Together, they create a richer matter record that supports better search, governance, and collaboration across legal teams.

• Combines legal context with AI-generated content insights
• Improves document discovery across active matters
• Supports more efficient collaboration between attorneys, paralegals, and records staff