PubTables-v2: A new large-scale dataset for full-page and multi-page table extraction

• URL: http://arxiv.org/abs/2512.10888v1
• Date: Thu, 11 Dec 2025 18:19:00 GMT
• Title: PubTables-v2: A new large-scale dataset for full-page and multi-page table extraction
• Authors: Brandon Smock, Valerie Faucon-Morin, Max Sokolov, Libin Liang, Tayyibah Khanam, Maury Courtland,
• Abstract summary: Table extraction is a key challenge in visual document understanding.<n>PubTables-v2 is the first large-scale benchmark for multi-page table structure recognition.<n>We use PubTables-v2 to create the Page-Object Table Transformer (POTATR), an image-to-graph extension of the Table Transformer to comprehensive page-level TE.
• Score: 1.2554129265335303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Table extraction (TE) is a key challenge in visual document understanding. Traditional approaches detect tables first, then recognize their structure. Recently, interest has surged in developing methods, such as vision-language models (VLMs), that can extract tables directly in their full page or document context. However, progress has been difficult to demonstrate due to a lack of annotated data. To address this, we create a new large-scale dataset, PubTables-v2. PubTables-v2 supports a number of current challenging table extraction tasks. Notably, it is the first large-scale benchmark for multi-page table structure recognition. We demonstrate its usefulness by evaluating domain-specialized VLMs on these tasks and highlighting current progress. Finally, we use PubTables-v2 to create the Page-Object Table Transformer (POTATR), an image-to-graph extension of the Table Transformer to comprehensive page-level TE. Data, code, and trained models will be released.

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