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Questions & Requests?

See original GitHub issue
  1. eval_relations(gt=[ground_truth_relations], res=[your_relations], cmp_blank=True)
  • As the objective is to find the merged relations of the neighboring cells, isn’t the blank relation comparison a wrong evaluation metric?
  1. this
  • That assumes every cell has unique text and case sensitive. Considering the fact this is only to generate relations for comparisons against the prediction. This may result in false negatives?
  1. Evaluation stats & trained models
  • Can we expect to get the evaluation stats & trained models getting published?

Issue Analytics

  • State:open
  • Created 3 years ago
  • Comments:27 (9 by maintainers)

github_iconTop GitHub Comments

Darenarcommented, Feb 4, 2021

Hi, everyone. For those who also struggles with the identification of boundary boxes in the pdf - I think I’ve got a solution. First of all, to read PDF correctly through python, you have to be sure that the size of loaded PDF is exactly the same as the original one. For example, I’ve been loading pages from pdf as images using pdf2image convert_from_path function. As the default, it uses parameter dpi as 200, which is appeared to be not correct. The thing you need to do is to identify the correct shape of the PDF, using for example PyPDF2 from , and then you could have used convert_from_path with parameter ```size`` specifying the correct shape of the pdf.

Another vital point here is that coordinates are different from those you wish to apply to your PIL.Image object, for example. The thing you have to do is too adjust your y1 and y2 by y1 = PAGE_HEIGHT-y2 and y2 = PAGE_HEIGHT-y1. It comes from the different measures of Y coordinates in python and in PDF itself.

I hope it will be handy to someone, cause I’ve struggled with it for almost the whole day!

kbrajwanicommented, Nov 23, 2020

@rmporsch @akshowhini @abhyantrika can you help me to inference the trained model. I have only image from which contains the table. so how can i create the other required files and get the structure of table from image.

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