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Computer Science > Computer Vision and Pattern Recognition

Title: Evaluating Table Structure Recognition: A New Perspective

Abstract: 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.
Comments: 4 pages, 2 figures, 1 table, 15th IAPR International Workshop on Document Analysis System (DAS 2022)
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2208.00385 [cs.CV]
  (or arXiv:2208.00385v1 [cs.CV] for this version)

Submission history

From: Tarun Kumar [view email]
[v1] Sun, 31 Jul 2022 07:48:36 GMT (186kb,D)

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