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• W2521665229 abstract "Building on recent advances in image caption generation and optical character recognition (OCR), we present a general-purpose, deep learning-based system to decompile an image into presentational markup. While this task is a well-studied problem in OCR, our method takes an inherently different, data-driven approach. Our model does not require any knowledge of the underlying markup language, and is simply trained end-to-end on real-world example data. The model employs a convolutional network for text and layout recognition in tandem with an attention-based neural machine translation system. To train and evaluate the model, we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup, as well as a synthetic dataset of web pages paired with HTML snippets. Experimental results show that the system is surprisingly effective at generating accurate markup for both datasets. While a standard domain-specific LaTeX OCR system achieves around 25% accuracy, our model reproduces the exact rendered image on 75% of examples." @default.
• W2521665229 created "2016-09-30" @default.
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• W2521665229 date "2016-09-16" @default.
• W2521665229 modified "2023-09-23" @default.
• W2521665229 title "What You Get Is What You See: A Visual Markup Decompiler." @default.
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• W2521665229 hasPublicationYear "2016" @default.
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