FontSearch

Type in natural language, find fonts that match your vibe!
Semantically Generated Fonts
Example of a fontsearch result with high granularity labelling
CLIP model to combine Font-Image embeddings for better retrieval

Inspiration

Frustrated by traditional font catalogs that require exact names or endless scrolling, we envisioned a tool that understands how designers naturally think about and describe fonts they need. What it does FontSearch uses natural language processing to match font descriptions with visual results. Type "playful handwritten font" or "professional serif" and instantly see matching fonts with previews, technical specs, and suggested use cases.

How we built it

Frontend: HTML5, TailwindCSS, JavaScript with responsive masonry layout Backend: Python API with font processing and embeddings Search: Vector database for semantic matching UI: Dynamic card system with expandable details and visual previews

Challenges we ran into

Implementing true masonry layout without disrupting neighboring columns Optimizing font preview loading for performance Creating accurate semantic search matches for font descriptions Balancing information density with clean UI

Accomplishments that we're proud of

Created intuitive natural language search for fonts Built responsive masonry grid that maintains visual harmony Developed smart tagging system for font characteristics Achieved fast search response times with large font database

What we learned

Semantic search implementation using vector DB Performance optimization for image-heavy applications User behavior patterns in font selection QLoRA Stable Diffusion fine tuning for generative examples.