Reproducible Label

For its first edition, the RRPR committee introduces the "Reproducible Label in Pattern Recognition" in order to highlight the reproducible aspects of the RRPR and ICPR works.

Who can apply?

All authors of accepted RRPR and/or ICPR papers.

Why apply?

The label proposed by the RRPR committee is an additional recognition of the work presented at the ICPR conference and ICPR Workshops. The label is a proof that your work can be reproduced from the git repository that will be indexed by RRPR. This is a major advantage for other researchers wanting to compare their works to yours: the label guarantees that the comparisons are performed with the author implementation used in the corresponding paper.

How to apply?

You need to provide an URL of a GitHub repository containing:

  • the source code allowing to reproduce all experiments of the paper,
  • the source code allowing to reproduce all figures of your paper,
  • a (set of) script file(s) allowing to install end execute your code,
  • a README file containing all compilation instructions.
  • a link or ref to the original associated paper.

The URL must be sent to :
If the original paper cannot be publicly broadcast or the public link is not yet known, please send a copy of your paper in this same email.

When to apply?

The submission is independent from ICPR and RRPR timelines: you can send your contribution before, during, or after these events. All submissions will be reviewed in submission order.

Which are the accepted languages/libraries/operating systems?

All languages and libraries are accepted. You just need to precise the used versions.
Concerning the operating systems, your code must compile on at least one of the followings:

  • Mac OSX 10.12 (Sierra)
  • Ubuntu 16.04 or later
  • Windows 10 or later

How is my submission reviewed?

The source code is NOT reviewed. We only follow the README instructions to compile the source code and execute the script(s). The results are compared to the paper results/figures and the label is given if they are identical. The list of "labelled papers" will be listed on this website and on the Github page of the label.

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