Table Extraction
9 papers with code • 0 benchmarks • 0 datasets
Table extraction involves detecting and recognizing a table's logical structure and content from its unstructured presentation within a document
Benchmarks
These leaderboards are used to track progress in Table Extraction
Most implemented papers
Deep learning for table detection and structure recognition: A survey
The goals of this survey are to provide a profound comprehension of the major developments in the field of Table Detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches.
PubTables-1M: Towards comprehensive table extraction from unstructured documents
microsoft/table-transformer
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CVPR 2022
We demonstrate that these improvements lead to a significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition.
GFTE: Graph-based Financial Table Extraction
Irene323/GFTE
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Tabular data is a crucial form of information expression, which can organize data in a standard structure for easy information retrieval and comparison.
Flexible Table Recognition and Semantic Interpretation System
Moreover, to incorporate the extraction of semantic information, we develop a graph-based table interpretation method.
ScanBank: A Benchmark Dataset for Figure Extraction from Scanned Electronic Theses and Dissertations
To the best of our knowledge, ScanBank is the first manually annotated dataset for figure and table extraction for scanned ETDs.
DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles
A crucial component in the curation of KB for a scientific domain (e. g., materials science, foods & nutrition, fuels) is information extraction from tables in the domain's published research articles.
Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents
ailab-unifi/gnn-tableextraction
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Tables are widely used in several types of documents since they can bring important information in a structured way.
CTE: A Dataset for Contextualized Table Extraction
We define the task of Contextualized Table Extraction (CTE), which aims to extract and define the structure of tables considering the textual context of the document.
Schema-Driven Information Extraction from Heterogeneous Tables
We use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data formats.
