Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensor Fields for Data Extraction from Chart Images: Bar Charts and
  Scatter Plots   Charts are an essential part of both graphicacy (graphical literacy), and
statistical literacy. As chart understanding has become increasingly relevant
in data science, automating chart analysis by processing raster images of the
charts has become a significant problem. Automated chart reading involves data
extraction and contextual understanding of the data from chart images. In this
paper, we perform the first step of determining the computational model of
chart images for data extraction for selected chart types, namely, bar charts,
and scatter plots. We demonstrate the use of positive semidefinite second-order
tensor fields as an effective model. We identify an appropriate tensor field as
the model and propose a methodology for the use of its degenerate point
extraction for data extraction from chart images. Our results show that tensor
voting is effective for data extraction from bar charts and scatter plots, and
histograms, as a special case of bar charts.

16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th