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.

翻译：大规模文档处理需要兼具准确性与高效性的上下文感知表格提取（TE）技术。然而，现有方法依赖数十亿参数、数百步自回归推理或昂贵的API调用。受此启发，我们提出页面对象表格变换器（POTATR），一款仅含2900万参数的轻量级图像到图模型，通过扩展表格变换器（TATR）实现上下文感知的页面级TE。在PubTables-v2单页基准测试中，POTATR超越了包括前沿多模态大语言模型在内的所有受测模型，在取得0.964 GriTS_Con指标的同时，运行速度提升逾130倍，计算成本降低约300倍。此外，POTATR的输出具有空间可定位性：每个识别元素均附带边界框，支持可视化验证与几何文本分配。因此，POTATR在与其他模型协同时仍能实现统一的页面级TE，通过外部OCR扩展至扫描文档处理，并借助跨页合并等技术支持全文档TE。代码与模型将开源发布。