# A Deep Factorization of Style and Structure in Fonts

arXiv: https://arxiv.org/abs/1910.00748 · HTML: https://mlsanity.com/paper/1910.00748/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2019.

## Built on by (most cited first, 15 of 15)

- [Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts](https://mlsanity.com/paper/2104.00887/) (2021)
- [XMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font Generation](https://mlsanity.com/paper/2204.05084/) (2022)
- [Few shot font generation via transferring similarity guided global style and quantization local style](https://mlsanity.com/paper/2309.00827/) (2023)
- [Which Parts Determine the Impression of the Font?](https://mlsanity.com/paper/2103.14216/) (2021)
- [Few-shot Font Generation with Weakly Supervised Localized Representations](https://mlsanity.com/paper/2112.11895/) (2021)
- [One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition](https://mlsanity.com/paper/2105.05300/) (2021)
- [Scalable Font Reconstruction with Dual Latent Manifolds](https://mlsanity.com/paper/2109.06627/) (2021)
- [Font Style Interpolation with Diffusion Models](https://mlsanity.com/paper/2402.14311/) (2024)
- [Contour Completion by Transformers and Its Application to Vector Font Data](https://mlsanity.com/paper/2304.13988/) (2023)
- [TrueType Transformer: Character and Font Style Recognition in Outline Format](https://mlsanity.com/paper/2203.05338/) (2022)
- [Neural Representation Learning for Scribal Hands of Linear B](https://mlsanity.com/paper/2108.04199/) (2021)
- [Font Representation Learning via Paired-glyph Matching](https://mlsanity.com/paper/2211.10967/) (2022)
- [Local Style Awareness of Font Images](https://mlsanity.com/paper/2310.06337/) (2023)
- [Total Disentanglement of Font Images into Style and Character Class Features](https://mlsanity.com/paper/2403.12784/) (2024)
- [Operational Latent Spaces](https://mlsanity.com/paper/2406.02699/) (2024)

## Abstract (arXiv)

We propose a deep factorization model for typographic analysis that disentangles content from style. Specifically, a variational inference procedure factors each training glyph into the combination of a character-specific content embedding and a latent font-specific style variable. The underlying generative model combines these factors through an asymmetric transpose convolutional process to generate the image of the glyph itself. When trained on corpora of fonts, our model learns a manifold over font styles that can be used to analyze or reconstruct new, unseen fonts. On the task of reconstructing missing glyphs from an unknown font given only a small number of observations, our model outperforms both a strong nearest neighbors baseline and a state-of-the-art discriminative model from prior work.