Evidence map›Paper›PMID 36286379›Full record

ArticleJournal of imaging2022

Cor

Jochen Büttner, Julius Martinetz, Hassan El-Hajj, Matteo Valleriani

Abstract read
In one paragraph

Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jochen BüttnerMax Planck Institute for the History of Science, Boltzmannstr. 22, 14195 Berlin, Germany.ORCID 0000-0003-1758-3153
Julius MartinetzMax Planck Institute for the History of Science, Boltzmannstr. 22, 14195 Berlin, Germany.ORCID 0000-0002-8305-7138
Hassan El-HajjMax Planck Institute for the History of Science, Boltzmannstr. 22, 14195 Berlin, Germany.ORCID 0000-0001-6931-7709
Matteo VallerianiMax Planck Institute for the History of Science, Boltzmannstr. 22, 14195 Berlin, Germany.ORCID 0000-0002-0406-7777

Funding

Federal Ministry of Education and Research 01IS18037A
6 · The paper itself

Abstract

Recent advances in object detection facilitated by deep learning have led to numerous solutions in a myriad of fields ranging from medical diagnosis to autonomous driving. However, historical research is yet to reap the benefits of such advances. This is generally due to the low number of large, coherent, and annotated datasets of historical documents, as well as the overwhelming focus on Optical Character Recognition to support the analysis of historical documents. In this paper, we highlight the importance of visual elements, in particular illustrations in historical documents, and offer a public multi-class historical visual element dataset based on the

Indexed as

artificial intelligencedatasetdigital humanitieshistorical illustrationsobject detectionsphaera

Identifiers

PMID36286379
PMCPMC9605005

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.