Tootfinder

QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents
Eliott Thomas, Mickael Coustaty, Aurelie Joseph, Gaspar Deloin, Elodie Carel, Vincent Poulain D'Andecy, Jean-Marc Ogier
https://arxiv.org/abs/2506.14568

QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents
Automating table extraction (TE) from business documents is critical for industrial workflows but remains challenging due to sparse annotations and error-prone multi-stage pipelines. While semi-supervised learning (SSL) can leverage unlabeled data, existing methods rely on confidence scores that poorly reflect extraction quality. We propose QUEST, a Quality-aware Semi-supervised Table extraction framework designed for business documents. QUEST introduces a novel quality assessment model that ev…