  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.
