Abstract:Table Extraction (TE) consists in extracting tables from PDF documents, in a structured format enabling automatic processing. While numerous TE tools exist, the variety of methods and techniques makes it difficult for users to choose the most appropriate one. We propose a novel benchmark for assessing end-to-end TE methods (from PDF to the final table) over 86k pages. We contribute an analysis of TE evaluation metrics, and a novel, rigorous evaluation process, which allows scoring each TE sub-task as well as end-to-end TE, and captures model uncertainty. Along with prior datasets, our benchmark comprises two new heterogeneous datasets of 39k samples. We run our benchmark on diverse models, including off-the-shelf libraries, tools, computer vision-based models and modern approaches using general and specialized vision language models. The results demonstrate that TE remains challenging: current methods suffer from a lack of generalizability when facing heterogeneous data, and from limitations in robustness and interpretability.
From: Pierre Senellart [view email]
[v1]
Thu, 20 Nov 2025 08:09:48 UTC (18,183 KB)
[v2]
Fri, 31 Jul 2026 06:07:17 UTC (22,842 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。