Monotype's catalogue grew faster than any model could be retrained. We built the matcher.
A designer photographs a typeface and needs its name. Neuramonks delivered identification across 300,000+ fonts that takes new releases on the fly, with no retraining when the catalogue grows.
- 300K+
- Styles searchable
- 80%
- Top-10 accuracy
- 95%
- Match precision
- 0
- Retraining for new fonts
Delivered for the L&D team
- 300K+
- Styles searchable
- 80%
- Top-10 accuracy
- 95%
- Match precision
- 0
- Retraining for new fonts
The Client's Problem
A catalogue that grows every week, and a model that would need retraining.
- Fonts identified by eye and by guesswork
- Matched from a photograph in real time
- New releases waiting on a model retrain
- Added to the index without retraining
- Matching limited by catalogue size
- 300,000+ styles searched at once
What We Delivered
Six pieces of work, one matcher.
- 01 · Deep Learning
- Font embedding model
- Turns any type sample into a comparable signature.
- 02 · Applied Research
- Retrain-free onboarding
- New fonts join the index without touching the model.
- 03 · Computer Vision
- Sample extraction
- Reads type from photographs, screenshots and scans.
- 04 · Data Engineering
- 300,000-style index
- Searches the full catalogue in real time.
- 05 · Applied AI
- Top-10 ranking
- Returns the closest matches, not one uncertain answer.
- 06 · Integration
- UI and API delivery
- Matching wired into the interfaces designers already use.
- Across all six · Catalogue and image data
- Catalogue protected
- The index is built without exposing the font files themselves.
- Encrypted end to end
- Uploaded samples and results encrypted in transit and at rest.
- Access-controlled by role
- Partners and internal teams see only their own scope.
Catalogue growing faster than your model?
We scope which pieces your build actually needs, in 30 minutes.
The Result
New fonts join the index the day they ship.
Because the model compares signatures rather than memorising fixed classes, a new release is indexed rather than trained in. The catalogue can grow every week without the matcher falling behind it.
0
Retraining needed when the catalogue grows, with 80% top-10 accuracy across 300,000+ styles.
How The Engagement Ran
Four phases, sample to API.
- Phase 1
- Discovery
- Matching accuracy and catalogue growth mapped with the team.
- Phase 2
- Embedding model
- Font signature model trained across the style range.
- Phase 3
- Index and ranking
- Real-time search and top-10 ranking built.
- Phase 4
- Onboarding and rollout
- Retrain-free onboarding, then UI and API release.
- Deep Learning
- Metric learning
- Computer Vision
- Vector index
- API delivery
Why Neuramonks
Why Monotype chose Neuramonks.
- Outcome-driven delivery
- Accuracy and scaling targets set before development started.
- Built to grow
- New fonts onboard without retraining the model.
- Designer-grade precision
- Matches good enough to act on, not merely plausible.
- Deployable on your terms
- On-premises or air-gapped where policy requires it.
Common Questions
What teams ask about this build.
How does AI identify a font from an image?
The sample is turned into a signature and compared against signatures for every style in the index, returning the closest matches rather than a single guess.
What happens when a new font is released?
It is added to the index directly. Because matching compares signatures rather than fixed classes, the model does not need retraining.
Why return ten matches instead of one?
Typefaces in a family differ subtly, so a ranked shortlist lets a designer pick the right one rather than trusting a single uncertain answer.
What did the engagement include?
The embedding model, sample extraction, the 300,000-style index, top-10 ranking, retrain-free onboarding and UI and API delivery.
How is the font catalogue protected?
The index is built without exposing the font files, and uploaded samples and results are encrypted in transit and at rest.
