cBAD: ICDAR2017 Competition on Baseline Detection

Diem, Markus; Kleber, Florian; Fiel, Stefan; Grüning, Tobias; Gatos, Basilis

doi:10.1109/ICDAR.2017.222

Record link:

https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:3-3384
http://hdl.handle.net/20.500.12708/881

Title:

cBAD: ICDAR2017 Competition on Baseline Detection

Citation:

Diem, M., Kleber, F., Fiel, S., Grüning, T., & Gatos, B. (2017). cBAD: ICDAR2017 Competition on Baseline Detection. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Kyoto, Japan. https://doi.org/10.1109/ICDAR.2017.222

CatalogPlus:

AC11365321

Publisher DOI:

10.1109/ICDAR.2017.222

Publication Type:

Inproceedings - Full-Paper Contribution

Konferenzbeitrag - Full-Paper Contribution

Language:

English

Authors:

Diem, Markus
Kleber, Florian
Fiel, Stefan
Grüning, Tobias
Gatos, Basilis

Organisational Unit:

E193 - Institut für Rechnergestützte Automation

ISBN:

9781538635865

Date (published):

2017

Publisher:

Kyoto, Japan

Keywords:

cBAD; baseline detection; text-line detection

Abstract:

The cBAD competition aims at benchmarking state-of-the-art baseline detection algorithms. It is in line with previous competitions such as the ICDAR 2013 Handwriting Segmentation Contest. A new, challenging, dataset was created to test the behavior of state-of-the-art systems on real world data. Since traditional evaluation schemes are not applicable to the size and modality of this dataset, we present a new one that introduces baselines to measure performance. We received submissions from five different teams for both tracks.

Additional information:

The final publication is available via <a href="https://doi.org/10.1109/ICDAR.2017.222" target="_blank">https://doi.org/10.1109/ICDAR.2017.222</a>.

License:

In Copyright

Appears in Collections:

Conference Paper