devmandan/broken-qr-codes-104

Broken QR Codes (104)

104 real QR code images that ordinary decoders fail on, with byte-level ground truth where a public reference decoder could establish it, geometric annotations, and per-image results for four reference decoders.

This is a test set, not a training set. Every image here was collected because something failed on it, so the distribution is deliberately adversarial rather than representative.

Why this is hard

78 of the 104 images are read by none of the four reference decoders: Apple Vision, Apple CIDetector, and WeChat's CNN and classical detectors. The best of them, Apple Vision, reads 21.

The failure modes are the interesting part: artistic QR codes where the module grid is deliberately obscured by generated imagery, physical damage, extreme perspective, low contrast, motion blur, partial occlusion, screen glare, and resolutions from 0.05 to 16 megapixels.

What's in each row

field	notes
image	the picture itself
file_name, image_id, width, height
has_payload, payload, payload_bytes_hex	26 of 104 have a payload: the images at least one reference decoder reads
qr_version	QR version, on 19 images
quad	the symbol's four corners in pixels, on 22 images
finder_centers	the three finder-pattern centres, on 19 images
u_px_per_module	pixels per module, on 19 images
quad_source	the reference decoder whose reading gave the geometry
solved_by_*	four booleans, one per reference decoder
n_decoders_solving	0–4

Only 26 images can be checked

A payload is given only where a reference decoder reads the code, so 26 is the ceiling for scoring. A decoder that reads one of the other 78 images cannot be credited from this dataset, because nothing here says what the right content is. Report the denominator you used.

payload and payload_bytes_hex are not equally populated: 26 rows carry a payload string and 20 carry the raw bytes. Where both exist they agree; where only the string exists, binary payloads may not round-trip through UTF-8, so compare bytes when you have them.

Reference decoder results

Measured on one machine (Apple M2, macOS 14.3.1), decode call only, image already resident in memory.

decoder	solves / 104	false	p50 µs
Apple Vision	21	0	11,195
Apple CIDetector	18	0	7,168
WeChat (CNN)	14	0	—
WeChat (classical)	13	0	—

Apple returns a string plus a raw codeword stream and is scored on both surfaces; WeChat is string-only.

Usage

from datasets import load_dataset

ds = load_dataset("devmandan/broken-qr-codes-104", split="test")

# the checkable subset: the only fair denominator
checkable = ds.filter(lambda r: r["has_payload"])                  # 26

# the images every reference decoder failed
unsolved  = ds.filter(lambda r: r["n_decoders_solving"] == 0)      # 78

# geometry-supervised subset
geo = ds.filter(lambda r: r["quad"] is not None)                   # 22

Provenance, licensing, and takedown

Please read this before redistributing.

The annotations in this dataset — payloads, quads, finder centres, versions, module scales, decoder results — are original work and are offered under CC BY 4.0.

The images are a different matter and are not uniformly owned by the publisher of this dataset. They were collected from public web sources as examples of QR codes that fail to decode, including artistic/generated QR codes whose individual authorship is not documented. They are published here for research and benchmarking, which is the purpose for which they were gathered. No claim of ownership is made over them.

If you own an image in this set and want it removed, open a discussion on this repository and it will be taken down.

Payload contents are real. Several decoded payloads are live third-party URLs that were encoded in the original codes, some carrying opaque identifiers. They are reproduced verbatim because a QR benchmark whose payloads have been rewritten cannot be used to check a decoder. Do not treat any URL here as endorsed, safe, or maintained, and be careful about resolving them automatically.

Citation

@misc{broken_qr_codes_104,
  title  = {Broken QR Codes (104): a hard real-world QR decoding benchmark},
  author = {Legut, Daniel},
  year   = {2026},
  url    = {https://huggingface.co/datasets/devmandan/broken-qr-codes-104}
}