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Lightweight POTATR model achieves SOTA table extraction

By PulseAugur Editorial · [2 sources] ·

Researchers have developed POTATR, a new lightweight image-to-graph model for extracting tables from documents. This 29 million parameter model significantly outperforms existing methods on the PubTables-v2 benchmark, achieving a GriTS_Con score of 0.964. POTATR is also considerably faster and more cost-effective than current large language models, with its output being spatially grounded for verification and further integration. AI

IMPACT Sets a new standard for efficient and accurate table extraction, potentially accelerating document processing workflows.

RANK_REASON New academic paper detailing a novel model and benchmark results.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Lightweight POTATR model achieves SOTA table extraction

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Brandon Smock, Libin Liang, Max Sokolov, Amrit Ramesh, Valerie Faucon-Morin, Tayyibah Khanam, Maury Courtland ·

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

    arXiv:2606.09788v1 Announce Type: new Abstract: 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.…

  2. arXiv cs.CV TIER_1 English(EN) · Maury Courtland ·

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

    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…

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