Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: De-rendering Stylized Texts   Editing raster text is a promising but challenging task. We propose to apply
text vectorization for the task of raster text editing in display media, such
as posters, web pages, or advertisements. In our approach, instead of applying
image transformation or generation in the raster domain, we learn a text
vectorization model to parse all the rendering parameters including text,
location, size, font, style, effects, and hidden background, then utilize those
parameters for reconstruction and any editing task. Our text vectorization
takes advantage of differentiable text rendering to accurately reproduce the
input raster text in a resolution-free parametric format. We show in the
experiments that our approach can successfully parse text, styling, and
background information in the unified model, and produces artifact-free text
editing compared to a raster baseline.

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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci