Abstract
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.
Original language | English (US) |
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State | Published - 2015 |
Event | 3rd International Conference on Learning Representations, ICLR 2015 - San Diego, United States Duration: May 7 2015 → May 9 2015 |
Conference
Conference | 3rd International Conference on Learning Representations, ICLR 2015 |
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Country/Territory | United States |
City | San Diego |
Period | 5/7/15 → 5/9/15 |
ASJC Scopus subject areas
- Education
- Linguistics and Language
- Language and Linguistics
- Computer Science Applications