Optimize your digital asset management with auto-tagging

by Veronika Altenbach

22. April 2024 4 minutes

In today's digital age, organizations must manage the metadata of a vast amount of digital assets. This metadata is crucial for efficient organization and retrieval of content, as well as for creating automated workflows. Often, the enrichment of digital assets is carried out manually, which can significantly reduce the efficiency of providing up-to-date content. In a fast-paced digital era, this can lead to significant financial losses. This is where the use of metadata mapping and artificial intelligence (AI) for automatic metadata tagging comes in.

Auto Tagging

Automatic tagging

Understanding and Benefits of Automatic Tagging

Automatic metadata tagging – or auto-tagging – is the process of automatically assigning descriptive metadata to a large volume of images. Each asset is classified through data extraction or analyzed by AI and provided with relevant tags, keywords, and specific descriptions. This process enriches digital assets with valuable information that also forms the basis for unlocking the full potential of a Digital Asset Management (DAM) system.

Automatic tagging is not exclusively dependent on AI; it can also be performed without it. For example, valuable information that is already stored by the camera when an asset is recorded can be automatically extracted and used. For instance, the season for which the image is relevant can be directly derived from the recording date.


The Importance of Tags and Why They Are Relevant

In the DAM world, metadata refers to a descriptive term, a keyword, asset-specific information such as copyright information, location of capture, etc., or descriptions of the content of the asset. Meaningful metadata not only helps to quickly find and reuse assets, but also facilitates the automation of workflows and the adherence to corporate policies and standards.

While a metadata mapper can read out fields such as

  • Creator (e.g., photographer)
  • GPS location
  • Capture date
  • Image size
  • Image format (landscape or portrait)
  • Copyright information
  • and other information stored in IPTC, EXIF, or XMP data,

AI services such as Microsoft Azure AI Services offer further possibilities for tagging assets:

  • Object recognition: A tag is created for each object found in the image
  • Recognition: Brand names and logos are recognized and reproduced
  • Identification: Background, foreground, and accent colors are identified
  • Tagging: Provision of information about recognizable objects such as living beings, landscapes, and actions that occur in an image
  • Image captioning: Creation of image descriptions, captions, and alt text for more accessibility
  • OCR recognition: Extraction of text from images or text documents for the automatic creation of alt text, captions, or image descriptions
  • Similarity search: Presents images that are similar to an uploaded image or documents that contain thematically similar text


The use of automatic tagging represents a huge advance in Digital Asset Management. This technology enables significant time savings and improves the organization and efficiency of asset management. This allows companies to fully exploit the full potential of their digital resources.

Are you ready to discover the benefits of auto-tagging for your business?

Then contact us and find out more

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