Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CG-DIQA: No-reference Document Image Quality Assessment Based on
  Character Gradient   Document image quality assessment (DIQA) is an important and challenging
problem in real applications. In order to predict the quality scores of
document images, this paper proposes a novel no-reference DIQA method based on
character gradient, where the OCR accuracy is used as a ground-truth quality
metric. Character gradient is computed on character patches detected with the
maximally stable extremal regions (MSER) based method. Character patches are
essentially significant to character recognition and therefore suitable for use
in estimating document image quality. Experiments on a benchmark dataset show
that the proposed method outperforms the state-of-the-art methods in estimating
the quality score of document images.

5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph