  We present a domain adaption framework to address a domain mismatch between
synthetic training and real-world testing data. We demonstrate our method on 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 font recognition methods (Chen et al. (2014)). In this paper, we
introduce a Convolutional Neural Network decomposition approach, leveraging a
large training corpus of synthetic data to obtain effective features for
classification. This is done using an adaptation technique based on a Stacked
Convolutional Auto-Encoder that exploits a large collection of unlabeled
real-world text images combined with synthetic data preprocessed in a specific
way. The proposed DeepFont method achieves an accuracy of higher than 80%
(top-5) on a new large labeled real-world dataset we collected.
