ScriptNet: ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD)
- 1. Computer Vision Lab, TU Wien
- 2. Computational Intelligence Technology Lab, University of Rostock
- 3. Computational Intelligence Laboratory, National Center of Scientific Research Demokritos
Description
This dataset contains the training and test set for the ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD).
A newly created freely available real world dataset consisting of 2035 annotated document page images that are collected from 9 different archives and form the basis of cBAD. Two competition tracks test different characteristics of the methods submitted. Track A [Simple Documents] is published with annotated text regions and tests therefore a method's quality of text line segmentation. The more challenging Track B [Complex Documents] provides only the page area. Hence, baseline detection algorithms need to correctly locate text lines in the presence of marginalia, tables, and noise.
The dataset comprises images with additional PAGE XMLs. The PAGE XMLs contain text regions and baseline annotations.
Competition Website: https://scriptnet.iit.demokritos.gr/competitions/5/
Version 3 is the version of the cBad competition
Version 4 contains also the page region and in case of a double-page the page split as separator.
Notes
Files
READ-ICDAR2017-cBAD-dataset-v4.zip
Files
(4.2 GB)
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md5:5004e86616187cae3f2d177baaabbfe4
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Additional details
References
- M. Diem, F. Kleber, S. Fiel, T. Grüning, and B. Gatos, cBAD: ICDAR2017 Competition on Baseline Detection, In proceedings of the International Conference on Document Analysis and Recognition 2017, in press
- T. Grüning, R. Labahn, M. Diem, F. Kleber, and S. Fiel, READ-BAD: A new dataset and evaluation scheme for baseline detection in archival documents," CoRR, vol. abs/1705.03311, 2017. [Online]. Available: http://arxiv.org/abs/1705.03311