Embedding Every Font with Neural Networks makes some Nice Structures (including a flower) [P]

A developer has been working on a font searching tool for about a year, utilizing pre-trained neural networks to create embeddings for each font. The process involves converting font glyphs into images, feeding them through a custom pre-trained neural network to obtain embeddings representing visual characteristics. These embeddings are then reduced using tSNE into XYZ and RGB channels for visualization as dots. Fonts with similar visual characteristics appear close in position or color, revealing clusters and paths of font types, with some visualizations forming interesting structures like a flower.

This project uniquely visualizes font embeddings as XYZ and RGB channels, using tSNE for better structural representation compared to PCA and UMAP.