Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning Approaches for Image Retrieval and Pattern Spotting in
  Ancient Documents   This paper describes two approaches for content-based image retrieval and
pattern spotting in document images using deep learning. The first approach
uses a pre-trained CNN model to cope with the lack of training data, which is
fine-tuned to achieve a compact yet discriminant representation of queries and
image candidates. The second approach uses a Siamese Convolution Neural Network
trained on a previously prepared subset of image pairs from the ImageNet
dataset to provide the similarity-based feature maps. In both methods, the
learned representation scheme considers feature maps of different sizes which
are evaluated in terms of retrieval performance. A robust experimental protocol
using two public datasets (Tobacoo-800 and DocExplore) has shown that the
proposed methods compare favorably against state-of-the-art document image
retrieval and pattern spotting methods.

5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph