Document Analysis and Recognition – ICDAR 2021
Springer International Publishing
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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Document Domain Randomization for Deep Learning Document Layout Extraction
|
|---|---|
| Book title |
Document Analysis and Recognition – ICDAR 2021
|
| Published in |
arXiv, September 2021
|
| DOI | 10.1007/978-3-030-86549-8_32 |
| Book ISBNs |
978-3-03-086548-1, 978-3-03-086549-8
|
| Authors |
Ling, Meng, Chen, Jian, Möller, Torsten, Isenberg, Petra, Isenberg, Tobias, Sedlmair, Michael, Laramee, Robert S., Shen, Han-Wei, Wu, Jian, Giles, C. Lee, Meng Ling, Jian Chen, Torsten Möller, Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Robert S. Laramee, Han-Wei Shen, Jian Wu, C. Lee Giles |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 2 | 17% |
| Librarian | 1 | 8% |
| Student > Ph. D. Student | 1 | 8% |
| Student > Doctoral Student | 1 | 8% |
| Researcher | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 5 | 42% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 42% |
| Agricultural and Biological Sciences | 1 | 8% |
| Engineering | 1 | 8% |
| Unknown | 5 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 11 July 2022.
All research outputs
#17,776,263
of 22,831,537 outputs
Outputs from arXiv
#439,570
of 937,493 outputs
Outputs of similar age
#290,249
of 426,534 outputs
Outputs of similar age from arXiv
#17,254
of 35,040 outputs
Altmetric has tracked 22,831,537 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 937,493 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 426,534 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35,040 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.