Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Text Region Extraction from Business Card Images for Mobile Devices   Designing a Business Card Reader (BCR) for mobile devices is a challenge to
the researchers because of huge deformation in acquired images, multiplicity in
nature of the business cards and most importantly the computational constraints
of the mobile devices. This paper presents a text extraction method designed in
our work towards developing a BCR for mobile devices. At first, the background
of a camera captured image is eliminated at a coarse level. Then, various rule
based techniques are applied on the Connected Components (CC) to filter out the
noises and picture regions. The CCs identified as text are then binarized using
an adaptive but light-weight binarization technique. Experiments show that the
text extraction accuracy is around 98% for a wide range of resolutions with
varying computation time and memory requirements. The optimum performance is
achieved for the images of resolution 1024x768 pixels with text extraction
accuracy of 98.54% and, space and time requirements as 1.1 MB and 0.16 seconds
respectively.

21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex