mlsanityData to 14 Oct 2026

Evaluating Table Structure Recognition: A New Perspective

arXiv 2208.00385 ↗First posted 31 Jul 2022cs.CV cs.LGcited by 1

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Abstract (arXiv)

Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust against the above disadvantages. We demonstrate the effectiveness of our metric against previous metrics through various examples.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 5 references that are in the map. First posted on arXiv 31 Jul 2022.

Builds on

3 of 5 references.

References · 3

  1. 2019Image-based table recognition: data, model, and evaluationcited by 237
  2. 2016A note on the triangle inequality for the Jaccard distancecited by 32
  3. 2021ICDAR 2021 Competition on Scientific Literature Parsingcited by 16

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Citing papers per year: 2022: 1.

1 paper.