QR-DN1.0 — Distorted and Noisy QRs

A mirror of QR-DN1.0 (Mendeley Data, DOI 10.17632/t2bdr663ms.2), repackaged as parquet with the five aligned image variants joined into single rows.

Released by its authors under CC BY 4.0, which is what makes this mirror straightforwardly permitted with attribution.

Configs

aligned — 1,500 train / 750 test

Five variants of the same sample, one row each. The upstream directories were verified to carry identical stems, set-equal across all five, so the join is exact rather than approximate — no row is dropped or padded.

column	upstream directory
target	target/
target_quad	target quad/
extracted_one	extracted One/
extracted_quad	extracted Quad/
extracted_voted	extracted Voted/

source_qr — 50 train / 25 test

The QR/ directory: source codes named ht1…htN, on a naming scheme unrelated to the aligned samples.

An honest limit on this card

The semantics of the five variants are not documented here, because this is a mirror and the packager did not establish which variant is input, which is ground truth, or what "One", "Quad" and "Voted" denote as methods. The names are preserved exactly as published so they can be matched against the paper. For the authoritative description, go to the DOI. If you want a card that explains the protocol, that should come from someone who has read the paper, not from the directory listing.

Usage

from datasets import load_dataset

ds = load_dataset("devmandan/qr-dn1.0", "aligned", split="train")
row = ds[0]
row["target"], row["extracted_voted"]      # PIL images, same sample

src = load_dataset("devmandan/qr-dn1.0", "source_qr", split="train")

Citation

@misc{qrdn1_2021,
  title     = {QR-DN1.0: A new Distorted and Noisy QRs dataset},
  author    = {Monfared, Milad and Koochari, Abbas and Monshinmotlagh, Radin},
  year      = {2021},
  publisher = {Mendeley Data},
  version   = {V2},
  doi       = {10.17632/t2bdr663ms.2}
}

Licence: CC BY 4.0 — https://creativecommons.org/licenses/by/4.0/ Please cite the authors above, not this mirror, when using the data.