Visuelle Suche in historischen Werken

Brantl, Markus; Ceynowa, Klaus; Meiers, Thomas; Wolf, Thomas

doi:10.1007/s13222-017-0250-0

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…In this regard, one of the main hurdles hindering the success of semantic visual element recognition within historical documents is their high variability, as well as the general scarcity of coherent historical datasets focused on visual element recognition, with only 11 out of the 56 historical document datasets mentioned in [ 15 ] containing graphical elements. Additionally, in the majority of cases where visual elements were recorded in these datasets, they were not classified according to their semantic classes [ 16 ].…”

Section: State Of the Artmentioning

confidence: 99%

CorDeep and the Sacrobosco Dataset: Detection of Visual Elements in Historical Documents

et al. 2022

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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 This is generally due to the low number of large, coherent, and annotated datasets of historical documents, as well as the overwhelming focus on Optical Character Recognition to support the analysis of historical documents. In this paper, we highlight the importance of visual elements, in particular illustrations in historical documents, and offer a public multi-class historical visual element dataset based on the Sphaera corpus. Additionally, we train an image extraction model based on YOLO architecture and publish it through a publicly available web-service to detect and extract multi-class images from historical documents in an effort to bridge the gap between traditional and computational approaches in historical studies.

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Section: State Of the Artmentioning

confidence: 99%