GROTOAP2  The Methodology of Creating a Large Ground Truth Dataset of Scientific Articles

Tkaczyk, Dominika; Szostek, Pawel; Bolikowski, Łukasz

doi:10.1045/november14-tkaczyk

Cited by 20 publications

(14 citation statements)

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“…Existing region-labeled datasets of scientific articles proved too noisy for our visual region detection method. Training the model with the full GROTOAP2 dataset (Tkaczyk et al, 2014), for example, yielded a best overall detection performance of 5.1% mean average precision (mAP) over all 22 labels. The key problems were that many of the regions were far too granular (e.g.…”

Section: Novel Labeled Datasetmentioning

confidence: 99%

Visual Detection with Context for Document Layout Analysis

Soto¹,

Yoo²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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We present 1) a work in progress method to visually segment key regions of scientific articles using an object detection technique augmented with contextual features, and 2) a novel dataset of region-labeled articles. A continuing challenge in scientific literature mining is the difficulty of consistently extracting high-quality text from formatted PDFs. To address this, we adapt the object-detection technique Faster R-CNN for document layout detection, incorporating contextual information that leverages the inherently localized nature of article contents to improve the region detection performance. Due to the limited availability of high-quality region-labels for scientific articles, we also contribute a novel dataset of region annotations, the first version of which covers 9 region classes and 822 article pages. Initial experimental results demonstrate a 23.9% absolute improvement in mean average precision over the baseline model by incorporating contextual features, and a processing speed 14x faster than a text-based technique. Ongoing work on further improvements is also discussed.

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Section: Novel Labeled Datasetmentioning

confidence: 99%

Visual Detection with Context for Document Layout Analysis

Soto¹,

Yoo²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…Table 3 provides a comparison of the DocBank to the previous document layout analysis datasets, including Article Regions (Soto and Yoo, 2019), GROTOAP2 (Tkaczyk et al, 2014), PubLayNet (Zhong et al, 2019), and TableBank (Li et al, 2019 19.36% Total 400,000 100.00% 50,000 100.00% 50,000 100.00% 500,000 100.00%…”

Section: Dataset Statisticsmentioning

confidence: 99%

DocBank: A Benchmark Dataset for Document Layout Analysis

Li¹,

Xu²,

Cui³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

144

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Document layout analysis usually relies on computer vision models to understand documents while ignoring textual information that is vital to capture. Meanwhile, high quality labeled datasets with both visual and textual information are still insufficient. In this paper, we present DocBank, a benchmark dataset that contains 500K document pages with fine-grained tokenlevel annotations for document layout analysis. DocBank is constructed using a simple yet effective way with weak supervision from the L A T E X documents available on the arXiv.com. With DocBank, models from different modalities can be compared fairly and multi-modal approaches will be further investigated and boost the performance of document layout analysis. We build several strong baselines and manually split train/dev/test sets for evaluation. Experiment results show that models trained on DocBank accurately recognize the layout information for a variety of documents. The DocBank dataset is publicly available at https: //github.com/doc-analysis/DocBank.

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“…There are few exceptions to this rule, for example, Arabic script, and such cases would not be handled properly by the algorithm. This observation is reflected in the distances counted for all zone pairs: the distance is calculated using the angle of the slope of the vector connecting 4 An example page from a scientific publication. The image shows the zones and their reading order zones.…”

Section: Reading Order Resolvingmentioning

confidence: 99%

“…As a result, PMC could not be directly used for training and evaluation of the individual steps, such as page segmentation and zone classification. For these tasks, we built GROTOAP [38] and GROTOAP2 [4] datasets.…”

Section: Datasets Preparationmentioning

confidence: 99%

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CERMINE: automatic extraction of structured metadata from scientific literature

Tkaczyk

Szostek

Fedoryszak

et al. 2015

IJDAR

Self Cite

150

136

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CERMINE is a comprehensive open-source system for extracting structured metadata from scientific articles in a born-digital form. The system is based on a modular workflow, whose loosely coupled architecture allows for individual component evaluation and adjustment, enables effortless improvements and replacements of independent parts of the algorithm and facilitates future architecture expanding. The implementations of most steps are based on supervised and unsupervised machine learning techniques, which simplifies the procedure of adapting the system to new document layouts and styles. The evaluation of the extraction workflow carried out with the use of a large dataset showed good performance for most metadata types, with the average F score of 77.5 %. CERMINE system is available under an open-source licence and can be accessed at http:// cermine.ceon.pl. In this paper, we outline the overall workflow architecture and provide details about individual steps implementations. We also thoroughly compare CERMINE to similar solutions, describe evaluation methodology and finally report its results. B Dominika Tkaczyk

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GROTOAP2 The Methodology of Creating a Large Ground Truth Dataset of Scientific Articles

Cited by 20 publications

References 0 publications

Visual Detection with Context for Document Layout Analysis

Visual Detection with Context for Document Layout Analysis

DocBank: A Benchmark Dataset for Document Layout Analysis

CERMINE: automatic extraction of structured metadata from scientific literature

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GROTOAP2  The Methodology of Creating a Large Ground Truth Dataset of Scientific Articles

Cited by 20 publications

References 0 publications

Visual Detection with Context for Document Layout Analysis

Visual Detection with Context for Document Layout Analysis

DocBank: A Benchmark Dataset for Document Layout Analysis

CERMINE: automatic extraction of structured metadata from scientific literature

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GROTOAP2 The Methodology of Creating a Large Ground Truth Dataset of Scientific Articles