[Paper] POTATR: A Lightweight Image-to-Graph Model for Page-Level Table Extraction

Source: arXiv - 2606.09788v1

Overview

Large-scale document processing requires contextually aware table extraction (TE) that is both accurate and efficient. Yet current approaches require billions of parameters, hundreds of autoregressive steps, or costly API inference. Motivated by this, we introduce the Page-Object Table Transformer (POTATR), a lightweight 29M parameter image-to-graph model that extends the Table Transformer (TATR) for contextualized page-level TE. POTATR outperforms all models tested on the PubTables-v2 Single Pages benchmark — including frontier MLLMs — achieving $\textrm{GriTS}_\textrm{Con}$ of 0.964 while running over 130$\times$ faster at roughly 300$\times$ lower cost. Further, POTATR’s output is spatially grounded: every recognized element has a bounding box, enabling visual verification and geometric text assignment. As a result, POTATR performs unified page-level TE while composing with other models, enabling extension to scanned documents via external OCR and to full-document TE via techniques like cross-page merging. Code and models will be released.

Key Contributions

This paper presents research in the following areas:

• cs.CV

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.CV.

Authors

• Brandon Smock
• Libin Liang
• Max Sokolov
• Amrit Ramesh
• Valerie Faucon-Morin
• Tayyibah Khanam
• Maury Courtland

Paper Information

• arXiv ID: 2606.09788v1
• Categories: cs.CV
• Published: June 8, 2026
• PDF: Download PDF