OpenText DAM (OTMM) - Google Vision AI Integration and Automation

Common Integration Use Cases Between OpenText DAM (OTMM) and Google Vision AI

• Automated image tagging for product and campaign assets
  OpenText DAM (OTMM) can send newly ingested product photos, campaign creatives, and event images to Google Vision AI for object, scene, and text detection. The extracted metadata can be written back to OTMM to improve search, filtering, and asset reuse. This reduces manual tagging effort for content teams and speeds up asset approval and distribution.
• OCR-based metadata extraction for packaging and label assets
  OTMM can route packaging images, product labels, brochures, and scanned collateral to Google Vision AI to extract text via OCR. The recognized text can be stored as searchable metadata in OTMM and linked to the related product record or campaign. This supports compliance review, multilingual content management, and faster retrieval of assets by text content.
• Brand logo detection for marketing governance
  Marketing teams can use Google Vision AI to detect logos in images and videos stored in OTMM, helping verify correct brand usage across campaign assets, partner materials, and event photography. Detected brand marks can be used to flag unauthorized logo placement, identify co-branded content, and support brand compliance workflows before assets are published.
• Facial detection and people-centric asset organization
  For museums, heritage collections, corporate events, and marketing photography, OTMM can send images to Google Vision AI to detect faces and group people-centric content more efficiently. The results can help create searchable collections by event, person, or subject category, while also supporting consent and rights review processes for public-facing content.
• Content moderation for user-generated and external submissions
  When OTMM receives externally sourced images or video thumbnails from agencies, partners, or user-generated content channels, Google Vision AI can screen for inappropriate or risky imagery before assets are approved. Detected issues such as explicit content or unsafe visuals can trigger review tasks in OTMM, reducing legal and reputational risk for marketing and communications teams.
• Smart catalog enrichment for e-commerce product imagery
  Product images stored in OTMM can be analyzed by Google Vision AI to identify visible attributes such as packaging type, objects, colors, and contextual cues. OTMM can then pass these attributes into downstream product information or commerce systems to improve catalog completeness, search relevance, and channel-specific merchandising. This is especially useful when product metadata is incomplete or inconsistent.
• Accessibility enhancement through descriptive metadata generation
  Google Vision AI can generate descriptive labels and text extraction results for images managed in OTMM, enabling more accessible asset delivery to websites, intranets, and digital channels. OTMM can store this enriched metadata for use in alt text, captions, and accessibility workflows, helping teams meet accessibility standards with less manual effort.
• Cross-channel asset discovery and reuse for broadcast and video workflows
  For broadcast stills, promotional clips, and event footage managed in OTMM, Google Vision AI can identify key frames, text overlays, landmarks, and visual themes. OTMM can use these insights to improve clip discovery, support editorial review, and accelerate repurposing of footage for social, web, and on-demand channels. This reduces time spent manually reviewing large media libraries.