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- W1956804535 abstract "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." @default.
- W1956804535 created "2016-06-24" @default.
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- W1956804535 date "2014-12-18" @default.
- W1956804535 modified "2023-09-22" @default.
- W1956804535 title "Decomposition-Based Domain Adaptation for Real-World Font Recognition" @default.
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- W1956804535 doi "https://doi.org/10.48550/arxiv.1412.5758" @default.
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