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Found 6 papers, 1 papers with code
Rethinking Detection Based Table Structure Recognition for Visually Rich Document Images
However, existing detection-based models usually cannot perform as well as other types of solutions regarding cell-level TSR metrics, such as TEDS, and the underlying reasons limiting the performance of these models on the TSR task are also not well-explored.
Table Detection for Visually Rich Document Images
Table Detection (TD) is a fundamental task to enable visually rich document understanding, which requires the model to extract information without information loss.
Revisiting Table Detection Datasets for Visually Rich Documents
Moreover, to enrich the data sources, we propose a new ICT-TD dataset using the PDF files of Information and Communication Technologies (ICT) commodities, a different domain containing unique samples that hardly appear in open datasets.
Efficient Information Sharing in ICT Supply Chain Social Network via Table Structure Recognition
Table Structure Recognition (TSR) aims to represent tables with complex structures in a machine-interpretable format so that the tabular data can be processed automatically.
Handling big tabular data of ICT supply chains: a multi-task, machine-interpretable approach
To transform the tabular data in electronic documents into a machine-interpretable format and provide layout and semantic information for information extraction and interpretation, we define a Table Structure Recognition (TSR) task and a Table Cell Type Classification (CTC) task.
Table Structure Recognition with Conditional Attention
Table Structure Recognition (TSR) problem aims to recognize the structure of a table and transform the unstructured tables into a structured and machine-readable format so that the tabular data can be further analysed by the down-stream tasks, such as semantic modeling and information retrieval.
