---
license: cc-by-nc-4.0
language:
- en
task_categories:
- image-to-image
tags:
- super-resolution
- scene-text
- text-aware-super-resolution
- reasoning
- benchmark
pretty_name: ReasonText
size_categories:
- n<1K
---

# ReasonText

[🌐 Project Page](https://jasonleex1995.github.io/TAISR_needs_Reasoning) · 📄 arXiv (coming soon) · [💻 Code](https://github.com/jasonleex1995/TAISR_needs_Reasoning)

**ReasonText** is the first benchmark for **text-aware image super-resolution (TAISR)** with the following two
characteristics:

1. **High quality** — all annotations are produced by humans, with no automatic pipeline.
2. **Per-instance difficulty** — each text instance is labeled by difficulty: **Level 1** (perception) and
   **Level 2** (reasoning-required).

## Contents

```
HR/                     # 513 high-resolution images (512×512 PNG)
LR/                     # 513 low-resolution images (128×128 PNG, ×4)
ReasonText-meta.json    # word-level annotations, keyed by image stem
```

`ReasonText-meta.json` maps each image stem (e.g. `DRealSR__Canon_10__256_01`) to:

```jsonc
{
  "hr_image": "HR/<stem>.png",
  "lr_image": "LR/<stem>.png",
  "annotations": [
    {
      "hr_coords":       [[x1,y1], ...],   // word-level polygon in HR pixel coordinates
      "text":            "BEST",           // ground-truth transcription
      "text_rotation":   0,                // 0 / 90 / 180 / 270 (counter-clockwise)
      "text_flipped":    false,
      "text_difficulty": "Level 1",        // Level 1 / Level 2 / Level 3
      "reason_types":    ["Context","Prior"] // Level 2 only; see "Reasoning types" below
    }
  ]
}
```

## Difficulty levels

Each text instance is labeled by how it can be read:

| Level | Type | Readable from | Instances |
|---|---|---|---|
| **Level 1** | perception | the cropped LR text region | 1,754 |
| **Level 2** | reasoning-required | the full LR image (via context, prior, or logic) | 709 |

Text that human annotators could not identify even from the full LR image is labeled as **Level 3** (total
1,522 instances), which is not the main focus of this benchmark.

### Reasoning types (Level 2)

For a Level 2 instance, `reason_types` records which cue makes the text recoverable:

- **Context** — image context
- **Prior** — prior knowledge
- **Logic** — logical reasoning

Different annotators may recover the same text through different cues, so `reason_types` is **not unique**: it is
the set of cues reported across annotators for that instance (a subset of the three).

## Citation

If you find our work useful for your research, please consider citing our paper:

```bibtex
<fill in BibTeX>
```

## License

ReasonText is released under **CC BY-NC 4.0** (non-commercial research use only).