Zobrazeno 1 - 10
of 472
pro vyhledávání: '"Arlazarov, A."'
In this paper, we introduce HoughToRadon Transform layer, a novel layer designed to improve the speed of neural networks incorporated with Hough Transform to solve semantic image segmentation problems. By placing it after a Hough Transform layer, "in
Externí odkaz:
http://arxiv.org/abs/2402.02946
Unfolder: Fast localization and image rectification of a document with a crease from folding in half
Presentation of folded documents is not an uncommon case in modern society. Digitizing such documents by capturing them with a smartphone camera can be tricky since a crease can divide the document contents into separate planes. To unfold the documen
Externí odkaz:
http://arxiv.org/abs/2312.00467
Low-bit quantized neural networks are of great interest in practical applications because they significantly reduce the consumption of both memory and computational resources. Binary neural networks are memory and computationally efficient as they re
Externí odkaz:
http://arxiv.org/abs/2205.09120
Autor:
Alexander Gayer, Vladimir V. Arlazarov
Publikováno v:
IEEE Access, Vol 12, Pp 170530-170540 (2024)
Recent research on text detection has focused on scenes “in the wild”, while there is still a demand for a fast and high-quality model for the document domain. Since document OCR is often run on embedded devices such as smartphones, scanners or e
Externí odkaz:
https://doaj.org/article/c263f385a8c248bdbcf91fc119b9d77c
In this paper, we study the problem of feature points description in the context of document analysis and template matching. Our study shows that the specific training data is required for the task especially if we are to train a lightweight neural n
Externí odkaz:
http://arxiv.org/abs/2109.04134
Autor:
Bulatov, Konstantin, Emelianova, Ekaterina, Tropin, Daniil, Skoryukina, Natalya, Chernyshova, Yulia, Sheshkus, Alexander, Usilin, Sergey, Ming, Zuheng, Burie, Jean-Christophe, Luqman, Muhammad Muzzamil, Arlazarov, Vladimir V.
Publikováno v:
Computer Optics, volume 46, issue 2, p. 252-270, 2022
Identity documents recognition is an important sub-field of document analysis, which deals with tasks of robust document detection, type identification, text fields recognition, as well as identity fraud prevention and document authenticity validatio
Externí odkaz:
http://arxiv.org/abs/2107.00396
The demand for on-device document recognition systems increases in conjunction with the emergence of more strict privacy and security requirements. In such systems, there is no data transfer from the end device to a third-party information processing
Externí odkaz:
http://arxiv.org/abs/2106.09987
Publikováno v:
In Materialia March 2024 33
Autor:
K.B. Bulatov, A.S. Ingacheva, M.I. Gilmanov, K. Kutukova, Z.V. Soldatova, A.V. Buzmakov, M.V. Chukalina, E. Zschech, V.V. Arlazarov
Publikováno v:
Компьютерная оптика, Vol 47, Iss 4, Pp 658-667 (2023)
The monitored tomographic reconstruction (MTR) with optimized photon flux technique is a pioneering method for X-ray computed tomography (XCT) that reduces the time for data acquisition and the radiation dose. The capturing of the projections in the
Externí odkaz:
https://doaj.org/article/95c5e0194b3e413d9df2158b97bc5dfa
Autor:
Marat Gilmanov, Konstantin Bulatov, Oleg Bugai, Anastasia Ingacheva, Marina Chukalina, Dmitrii Nikolaev, Vladimir Arlazarov
Publikováno v:
PLoS ONE, Vol 19, Iss 7, p e0307231 (2024)
Monitored tomographic reconstruction (MTR) is a potentially powerful tool for dose and time reduction in computed tomography scanning. We are the first to study the issue of practical implementation of MTR protocols in current-generation real-life in
Externí odkaz:
https://doaj.org/article/1a7b1008cf644bd6aeef98abb563ffb6