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TableBank: A Benchmark Dataset for Table Detection and Recognition

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arxiv 1903.01949 v2 pith:ZA67IL5Q submitted 2019-03-05 cs.CV

Minghao Li , Lei Cui , Shaohan Huang , Furu Wei , Ming Zhou , Zhoujun Li This is my paper
classification cs.CV
keywords tablebankdetectionrecognitiontabledatasetmodelsavailabledeep
verification ladder T0 review T1 audit T2 compute T3 formal
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We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand human-labeled examples, which is difficult to generalize on real-world applications. With TableBank that contains 417K high quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks. We make TableBank publicly available and hope it will empower more deep learning approaches in the table detection and recognition task. The dataset and models are available at \url{https://github.com/doc-analysis/TableBank}.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Benchmarking Table Extraction from Heterogeneous Scientific PDF Documents

    cs.DB 2025-11 conditional novelty 6.0 of 10

    A new benchmark with two new datasets and end-to-end metrics shows that table extraction from PDFs is still unreliable across heterogeneous layouts.

  2. TEN: Table Explicitization, Neurosymbolically

    cs.CL 2025-08 unverdicted novelty 6.0 of 10

    A neurosymbolic system with structural decomposition prompting and a checker-driven self-debug loop improves table extraction from semistructured text over purely neural baselines.

  3. TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding

    cs.AI 2025-06 conditional novelty 6.0 of 10

    TableMoE is a multimodal table model whose role-aware router sends table tokens to HTML, JSON, and code experts and reports state-of-the-art results on its own WildStruct benchmarks and MMMU-Table.

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