Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Deep Representations for Word Spotting Under Weak Supervision   Convolutional Neural Networks have made their mark in various fields of
computer vision in recent years. They have achieved state-of-the-art
performance in the field of document analysis as well. However, CNNs require a
large amount of annotated training data and, hence, great manual effort. In our
approach, we introduce a method to drastically reduce the manual annotation
effort while retaining the high performance of a CNN for word spotting in
handwritten documents. The model is learned with weak supervision using a
combination of synthetically generated training data and a small subset of the
training partition of the handwritten data set. We show that the network
achieves results highly competitive to the state-of-the-art in word spotting
with shorter training times and a fraction of the annotation effort.

3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math