Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Segmentation-Free Approaches for Handwritten Numeral String Recognition   This paper presents segmentation-free strategies for the recognition of
handwritten numeral strings of unknown length. A synthetic dataset of touching
numeral strings of sizes 2-, 3- and 4-digits was created to train end-to-end
solutions based on Convolutional Neural Networks. A robust experimental
protocol is used to show that the proposed segmentation-free methods may reach
the state-of-the-art performance without suffering the heavy burden of
over-segmentation based methods. In addition, they confirmed the importance of
introducing contextual information in the design of end-to-end solutions, such
as the proposed length classifier when recognizing numeral strings.

17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc