↓ Skip to main content

Deep Vectorization of Technical Drawings

Overview of attention for article published in arXiv, March 2020
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
6 X users
patent
1 patent

Citations

dimensions_citation
40 Dimensions

Readers on

mendeley
90 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Deep Vectorization of Technical Drawings
Published in
arXiv, March 2020
DOI 10.1007/978-3-030-58601-0_35
Authors

Vage Egiazarian, Oleg Voynov, Alexey Artemov, Denis Volkhonskiy, Aleksandr Safin, Maria Taktasheva, Denis Zorin, Evgeny Burnaev

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 90 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 18%
Student > Master 15 17%
Student > Bachelor 5 6%
Researcher 4 4%
Lecturer 3 3%
Other 6 7%
Unknown 41 46%
Readers by discipline Count As %
Computer Science 30 33%
Engineering 6 7%
Environmental Science 1 1%
Agricultural and Biological Sciences 1 1%
Economics, Econometrics and Finance 1 1%
Other 5 6%
Unknown 46 51%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 August 2024.
All research outputs
#6,748,296
of 26,579,895 outputs
Outputs from arXiv
#108,745
of 1,011,406 outputs
Outputs of similar age
#119,737
of 393,058 outputs
Outputs of similar age from arXiv
#3,530
of 21,812 outputs
Altmetric has tracked 26,579,895 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,011,406 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 393,058 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 21,812 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.