Named for the person who knows the collection best. Built to help them do that work at a scale no team could match by hand.
A finished capture serves as a starting point, not a conclusion. DT Registrar is DT DigiLabs’ object recognition and metadata tool — deliberately named after museum registrars, the professionals with the deepest knowledge of a collection. It gives registrars and metadata specialists a way to apply that expertise across tens of thousands of images at once, instead of one record at a time — turning years of accumulated, undescribed images into a collection that’s actually searchable.
What Makes It Different
Generic image-tagging systems identify broad categories like “a car” or “a person” — unhelpful when every image in your collection already contains one. DT Registrar’s strength is contextual specificity, tuned to your collection’s actual vocabulary rather than generic labels, so the metadata it generates reads like it came from someone who already knows the collection.
- Context-aware recognition. Identifies image contents at a level of specificity generic tools can’t reach, using the terminology and cataloging fields your collection actually needs, generated at a pace no team could match by hand.
- Strategic omission. The tool deliberately leaves things out. Indiscriminate tagging buries the metadata your team actually needs under noise; what to extract and what to skip is set collection by collection, around your registrar’s own priorities — not a generic checklist.
What It Does
- Automatically identifies and tags objects and materials with collection-specific precision — so your team isn’t typing it in by hand
- Generates structured metadata customized to your cataloging framework, ready for a registrar to review and approve rather than author from scratch
- Performs context-aware analysis using your collection’s own vocabulary
- Converts thousands of undescribed images into searchable content in a fraction of the time manual entry would take
Who This Helps
DT Registrar is built for the registrars, catalogers, and metadata specialists working inside:
- Natural history collections — species and specimen identification at scale
- Museums — artifact classification and cataloging
- Sports and media archives — equipment, uniforms, logos, and venues
- Any institution whose registrar is sitting on a large “dark” (undescribed) digital collection
NASCAR Hall of Fame Case Study
The clearest working example involves hundreds of thousands of racing photographs at the NASCAR Hall of Fame. Staff needed structured extraction — car make, model, number, and primary sponsor — to let researchers find a specific image instead of browsing a binder labeled only with a year.
The engineering challenge required masking individual cars before any data could be extracted, particularly difficult when vehicles overlapped on track. Car recognition started with general automotive AI, then was refined using 100 of the Hall’s own expert-labeled photographs — teaching the model the specific vehicles in that collection, using the Hall’s own expertise as the training signal.
Notably, the team could also extract secondary sponsor decals — and turned it off. Small sponsor logos appeared on nearly every car for years; including them would have buried the searches people actually run. As Curator of Collections Siera Erazo put it: “It’s too much data, but it’s really cool that it can do it.” It’s a good illustration of how the tool is meant to work day to day: it surfaces what’s possible, and the registrar decides what’s actually useful for the collection.
A single car with basic make/model metadata can narrow an unlabeled photo down to specific races, decades after it was taken — the kind of breadcrumb DT Registrar is built to generate, so staff spend their time on the research those breadcrumbs make possible, not on data entry.
Responsible AI
DT Registrar suggests metadata for your team to review and approve — giving your registrar a running start on cataloging decisions, at a scale no team could reach by hand.
Integration with Existing Collections
DT Registrar operates on existing digital collections — whether DT DigiLabs captured them or your institution already had them — making it practical for converting years of undescribed scans into searchable assets.
The tool also leverages the institutional context your registrar has already built: box- and folder-level metadata, controlled vocabularies, collection documentation, and prior research. At the NASCAR Hall of Fame, the team’s own historic race database and folder-level metadata (year, specific race) pushed car-level accuracy close to 100% — and even flagged likely mislabeled folders for expert review. The registrar’s own institutional memory drives the accuracy as much as the underlying model does.
Pairs Well With
- Digitization & Capture — start with a fresh capture, then classify and catalog
- DT Identify — combine object metadata with identity search for full collection search
If your registrar or metadata specialist has more images than hours to describe them by hand, DT Registrar can help them work at the scale your collection demands. Talk to our team about your collection.