Azure Computer Vision - Consonance Integration and Automation

Common Integration Use Cases Between Azure Computer Vision and Consonance

1. Automated cover image tagging for title records

Data flow: Azure Computer Vision ? Consonance

When cover art, jacket images, or marketing visuals are uploaded to a DAM or shared workspace, Azure Computer Vision can identify objects, scenes, text, and visual attributes, then pass structured tags into Consonance and attach them to the relevant title record. This reduces manual metadata entry for editorial and marketing teams and improves searchability across the publishing catalog.

• Auto-populate cover metadata such as genre cues, visual themes, and dominant colors
• Link image-derived tags to title, imprint, and campaign records in Consonance
• Improve internal discovery of assets for sales sheets, catalogs, and retailer submissions

2. OCR extraction from manuscript scans and production documents

Data flow: Azure Computer Vision ? Consonance

Publishing teams often receive scanned contracts, annotated proofs, author forms, and legacy documents. Azure Computer Vision can extract text from these files and send the output to Consonance for indexing, workflow routing, or attachment to the correct project or title. This supports faster processing of production paperwork and reduces rekeying errors.

• Extract text from scanned rights agreements, approvals, and proof notes
• Store OCR output against the relevant manuscript or title workflow
• Enable faster search across production and rights documentation

3. Rights and permissions review for image-based content

Data flow: Azure Computer Vision ? Consonance

For illustrated books, celebrity content, or photo-heavy titles, Azure Computer Vision can detect faces, logos, and other identifiable elements in images submitted for publication. Consonance can then route the title to rights or legal review when potentially sensitive content is detected, helping publishers avoid clearance issues before publication.

• Flag images containing recognizable people, brands, or copyrighted material
• Trigger rights review tasks in Consonance based on detected content
• Reduce publication risk and support compliance workflows

4. Accessibility metadata generation for digital publishing

Data flow: Azure Computer Vision ? Consonance

Azure Computer Vision can generate descriptive metadata for images used in ebooks, audiobooks, and companion digital assets. Consonance can store this information alongside title metadata and production status so accessibility requirements are addressed earlier in the publishing workflow.

• Generate alt-text suggestions for illustrations, charts, and cover images
• Attach accessibility metadata to digital title records in Consonance
• Support inclusive publishing standards and reduce late-stage remediation

5. Automated quality checks for production assets

Data flow: Azure Computer Vision ? Consonance

Before a book moves to print or digital release, Azure Computer Vision can inspect supplied artwork and production files for issues such as low-quality scans, missing text, or unexpected visual elements. Consonance can use these findings to create exceptions, assign corrective tasks, and prevent defective assets from advancing in the workflow.

• Detect image quality issues in jacket art and interior illustrations
• Identify missing or unreadable text in submitted production files
• Route exceptions to designers, production editors, or vendors in Consonance

6. Metadata enrichment for backlist digitization programs

Data flow: Azure Computer Vision ? Consonance

When publishers digitize backlist titles or archive legacy assets, Azure Computer Vision can analyze old cover scans, promotional images, and printed materials to extract usable metadata. Consonance can then use that data to enrich title records, improving catalog completeness and supporting reissue, licensing, and discoverability initiatives.

• Extract title text and visual cues from archived cover images
• Fill gaps in legacy metadata for older titles
• Support faster reactivation of backlist titles for new formats and markets

7. Bi-directional workflow status and asset synchronization

Data flow: Consonance ? Azure Computer Vision and Azure Computer Vision ? Consonance

Consonance can send title, project, and asset context to Azure Computer Vision so the service analyzes the correct files with the right business rules. In return, the extracted metadata, warnings, and classifications can be written back to Consonance to update task status, approval gates, and title completeness indicators. This creates a closed-loop workflow between editorial, production, and asset intelligence.

• Use Consonance title IDs and workflow stage to drive image analysis rules
• Return analysis results to update task completion and exception handling
• Improve coordination between editorial, production, and DAM teams