Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Local Binary Pattern for Word Spotting in Handwritten Historical
  Document   Digital libraries store images which can be highly degraded and to index this
kind of images we resort to word spot- ting as our information retrieval
system. Information retrieval for handwritten document images is more
challenging due to the difficulties in complex layout analysis, large
variations of writing styles, and degradation or low quality of historical
manuscripts. This paper presents a simple innovative learning-free method for
word spotting from large scale historical documents combining Local Binary
Pattern (LBP) and spatial sampling. This method offers three advantages:
firstly, it operates in completely learning free paradigm which is very
different from unsupervised learning methods, secondly, the computational time
is significantly low because of the LBP features which are very fast to
compute, and thirdly, the method can be used in scenarios where annotations are
not available. Finally we compare the results of our proposed retrieval method
with the other methods in the literature.

22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math