# Real-World Font Recognition Using Deep Network and Domain Adaptation

arXiv: https://arxiv.org/abs/1504.00028 · HTML: https://mlsanity.com/paper/1504.00028/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2015.

## Built on by (most cited first, 1 of 1)

- [Texture or Semantics? Vision-Language Models Get Lost in Font Recognition](https://mlsanity.com/paper/2503.23768/) (2025)

## Abstract (arXiv)

We address a challenging fine-grain classification problem: recognizing a font style from an image of text. In this task, it is very easy to generate lots of rendered font examples but very hard to obtain real-world labeled images. This real-to-synthetic domain gap caused poor generalization to new real data in previous methods (Chen et al. (2014)). In this paper, we refer to Convolutional Neural Networks, and use an adaptation technique based on a Stacked Convolutional Auto-Encoder that exploits unlabeled real-world images combined with synthetic data. The proposed method achieves an accuracy of higher than 80% (top-5) on a real-world dataset.