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- W2626873800 abstract "We present a neural encoder-decoder model to convert images into presentational markup based on a scalable coarse-to-fine attention mechanism. Our method is evaluated in the context of image-to-LaTeX generation, and we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup. We show that unlike neural OCR techniques using CTC-based models, attention-based approaches can tackle this non-standard OCR task. Our approach outperforms classical mathematical OCR systems by a large margin on in-domain rendered data, and, with pretraining, also performs well on out-of-domain handwritten data. To reduce the inference complexity associated with the attention-based approaches, we introduce a new coarse-to-fine attention layer that selects a support region before applying attention." @default.
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- W2626873800 date "2016-09-16" @default.
- W2626873800 modified "2023-10-03" @default.
- W2626873800 title "Image-to-Markup Generation with Coarse-to-Fine Attention" @default.
- W2626873800 hasPublicationYear "2016" @default.
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