TableBank: A Benchmark Dataset for Table Detection and Recognition

Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou +1 more
3/5/2019
cs.CV

Official code: doc-analysis/TableBank (1,084 stars, Apache-2.0, last commit 2024-08-12).

Abstract

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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Code Implementations(1)

Apache-2.0

TableBank: A Benchmark Dataset for Table Detection and Recognition

1,084145Mar 5, 20192 years agoApache-2.0

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Cite this paper

@article{li2019tablebank,
  title  = {TableBank: A Benchmark Dataset for Table Detection and Recognition},
  author = {Minghao Li and Lei Cui and Shaohan Huang and Furu Wei and Ming Zhou and Zhoujun Li},
  year   = {2019},
  eprint = {1903.01949},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1903.01949}
}

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