Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HoughNet: neural network architecture for vanishing points detection   In this paper we introduce a novel neural network architecture based on Fast
Hough Transform layer. The layer of this type allows our neural network to
accumulate features from linear areas across the entire image instead of local
areas. We demonstrate its potential by solving the problem of vanishing points
detection in the images of documents. Such problem occurs when dealing with
camera shots of the documents in uncontrolled conditions. In this case, the
document image can suffer several specific distortions including projective
transform. To train our model, we use MIDV-500 dataset and provide testing
results. The strong generalization ability of the suggested method is proven
with its applying to a completely different ICDAR 2011 dewarping contest. In
previously published papers considering these dataset authors measured the
quality of vanishing point detection by counting correctly recognized words
with open OCR engine Tesseract. To compare with them, we reproduce this
experiment and show that our method outperforms the state-of-the-art result.

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The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph