Table headers: An entrance to the data mine

Nagy, George; Seth, Sharad

doi:10.1109/icpr.2016.7900270

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…TableDataExtractor returns this standardized table as the output, where each row corresponds to one data cell of the original table, and the columns represent categories to which the data entry belongs. TableDataExtractor is built upon the MIPS (Minimum Indexing Point Search) algorithm developed by Embley et al, 32 and extensions to it, developed by Nagy and Seth, 33 which have been further extended to enable a higher degree of automation, to include support for multiple tables within one table and to automatically resolve footnotes. The algorithms have also been adapted to function in an iterative fashion, which enables the standardization of data in the stub header of the table (Figure 4c).…”

Section: ■ Automated Parsingmentioning

confidence: 99%

ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science

Mavračić

Court

Isazawa

et al. 2021

J. Chem. Inf. Model.

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The ever-growing abundance of data found in heterogeneous sources, such as scientific publications, has forced the development of automated techniques for data extraction. While in the past, in the physical sciences domain, the focus has been on the precise extraction of individual properties, attention has recently been devoted to the extraction of higher-level relationships. Here, we present a framework for an automated population of ontologies. That is, the direct extraction of a larger group of properties linked by a semantic network. We exploit data-rich sources, such as tables within documents, and present a new model concept that enables data extraction for chemical and physical properties with the ability to organize hierarchical data as nested information. Combining these capabilities with automatically generated parsers for data extraction and forward-looking interdependency resolution, we illustrate the power of our approach via the automatic extraction of a crystallographic hierarchy of information. This includes 18 interrelated submodels of nested data, extracted from an evaluation set of scientific articles, yielding an overall precision of 92.2%, across 26 different journals. Our method and associated toolkit, ChemDataExtractor 2.0, offers a key step toward the seamless integration of primary literature sources into a data-driven scientific framework.

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Section: ■ Automated Parsingmentioning

confidence: 99%

ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science

Mavračić

Court

Isazawa

et al. 2021

J. Chem. Inf. Model.

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show abstract

“…Traditional layout analysis can be broadly classified into three methods: top-down methods [19], bottom-up methods [20], and hybrid methods [21]. With the continuous development of machine learning and neural network technology, emerging analysis methods have appeared in the field of layout analysis, mainly based on machine learning.…”

Section: Traditional Layout Analysis Methodsmentioning

confidence: 99%

Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

Pu,

Liu,

Chen

et al. 2023

Applied Sciences

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With the development of the information age, the layout image is no longer a simple combination of text and graphics, but covers the complex layout image obtained from text, graphics, images and other layout elements through the process of artistic design, pre-press processing, typesetting, and so on. At present, the field of aesthetic-quality assessment mainly focuses on photographic images, and the aesthetic-quality assessment of complex layout images is rarely reported. However, the design of complex layout images such as posters, packaging labels, advertisements, etc., cannot be separated from the evaluation of aesthetic quality. In this paper, layout analysis is performed on complex layout images. Traditional and deep-learning-based methods for image layout analysis and aesthetic-quality assessment are reviewed and analyzed. Finally, the features, advantages and applications of common image aesthetic-quality assessment datasets and layout analysis datasets are compared and analyzed. Limitations and future perspectives of aesthetic assessment of complex layout images are discussed in relation to layout analysis and aesthetic characteristics.

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“…Kise et al [3] used the connected component approach to obtain candidate boundaries of regions for layout segmentation using Voronoi diagrams. Nagy et al [4] cut after projection in the X-Y direction, which is only applicable to structured text with fixed text area and line spacing. This method is sensitive to boundary noise and demands high document quality.…”

Section: Related Workmentioning

confidence: 99%

The YOLO model that still excels in document layout analysis

Deng,

Ibrayim,

Hamdulla

et al. 2023

Preprint

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Document layout analysis can help people better understand and use the information in a document. However, the diversity of document layouts and considerable variation in aspect ratios among document objects pose significant challenges. In this study, we designed the Multi-Convolutional Deformable Separation (MCDS) module as the main structure of the network, using the YOLO model as a baseline. Integration of this module into the Backbone and Neck layers enhances the image feature extraction process significantly. Moreover, we incorporate ParNet-Attention to direct the network's focus toward document objects through parallel networks, thereby facilitating a more exhaustive feature extraction. To optimize the model's predictive potential, the Decouple Fusion Head (DFH) is employed within the Head layer. This technique leverages multi-scale features based on the decoupled head, thereby enhancing the accuracy of predictions. Our proposed model achieves remarkable performance on three distinct public datasets with varying characteristics, namely ICDAR-POD, PubLayNet, and IIIT-AR-13K. Notably, in ICDAR-POD, both IoU$_{0.6}$ and IoU$_{0.8}$ achieve the optimal mean Average Precision (mAP), 96.2 and 94.4, respectively.

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Table headers: An entrance to the data mine

Cited by 11 publications

References 20 publications

ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science

ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science

Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

The YOLO model that still excels in document layout analysis

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