Three Benchmark Datasets for Scholarly Article Layout Analysis

This dataset contains three benchmark datasets as part of the scholarly output of and ICDAR 2021 paper: Meng Ling, Jian Chen, Torsten Moller, Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Robert S. Laramee, Han-Wei Shen, Jian Wu, and C. Lee Giles, Document Domain Randomization for Deep Learning Document Layout Extraction, 16th International Conference on Document Analysis and Recognition (ICDAR) 2021. September 5-10, Lausanne, Switzerland. This dataset contains nine class lables: abstract, algorithm, author, body text, caption, equation, figure, table, and title. * Dataset 1: CS-150x, is an extension of the classical benchmark dataset CS-150 from three classes (figure, table, and caption) to nine classes, 1176 pages, Clark, C., Divvala, S.: Looking beyond text: Extracting figures, tables and captions from com- puter science papers. In: Workshops at the 29th AAAI Conference on Artificial Intelligence (2015), https://aaai.org/ocs/index.php/WS/AAAIW15/paper/view/10092.* Dataset 2: ACL300, has 300 randomly sampled articles (or 2508 pages) from the 55,759 papers scraped from the ACL anthology website; https://www.aclweb.org/anthology/.* Dataset 3: VIS300, has about 10% (or 2619 pages) of the document pages in randomly partitioned articles from 26,350 VIS paper pages, . Chen, J., Ling, M., Li, R., Isenberg, P., Isenberg, T., Sedlmair, M., Möller, T., Laramee, R.S., Shen, H.W., Wünsche, K., Wang, Q.: VIS30K: A collection of figures and tables from IEEE visualization conference publications. IEEE Trans. Vis. Comput. Graph. 27 (2021), to appear doi: 10.1109/TVCG.2021.3054916 This dataset is also available online at https://web.cse.ohio-state.edu/~chen.8028/ICDAR2021Benchmark/.