---
license: other
license_name: mixed-annotations-cc-by-4.0
license_link: LICENSE
pretty_name: Broken QR Codes (104) — a hard real-world QR benchmark
task_categories:
- image-to-text
- object-detection
tags:
- qr-code
- barcode
- benchmark
- document-understanding
- computer-vision
- robustness
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: data/**
---

# 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

```python
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

```bibtex
@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}
}
```