Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Glyph-driven Topology Enhancement Network for Scene Text Recognition   Attention-based methods by establishing one-dimensional (1D) and
two-dimensional (2D) mechanisms with an encoder-decoder framework have
dominated scene text recognition (STR) tasks due to their capabilities of
building implicit language representations. However, 1D attention-based
mechanisms suffer from alignment drift on latter characters. 2D attention-based
mechanisms only roughly focus on the spatial regions of characters without
excavating detailed topological structures, which reduces the visual
performance. To mitigate the above issues, we propose a novel Glyph-driven
Topology Enhancement Network (GTEN) to improve topological features
representations in visual models for STR. Specifically, an unsupervised method
is first employed to exploit 1D sequence-aligned attention weights. Second, we
construct a supervised segmentation module to capture 2D ordered and pixel-wise
topological information of glyphs without extra character-level annotations.
Third, these resulting outputs fuse enhanced topological features to enrich
semantic feature representations for STR. Experiments demonstrate that GTEN
achieves competitive performance on IIIT5K-Words, Street View Text,
ICDAR-series, SVT Perspective, and CUTE80 datasets.

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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg