Datasets:

Funovate-Technology
/

aifont-text-rendering

---
license: cc-by-4.0
pretty_name: AIFont text rendering in AI images
language:
- en
- de
- es
- pt
- zh
- ja
- ar
task_categories:
- text-to-image
- image-to-text
tags:
- text-rendering
- typography
- evaluation
- multilingual
- image-generation
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: generations.jsonl
---
> Mirror of [Funovate-Technology/aifont-text-rendering](https://github.com/Funovate-Technology/aifont-text-rendering) (canonical copy, DOI [10.5281/zenodo.23815210](https://doi.org/10.5281/zenodo.23815210)). Corrections go to the GitHub repo's issues. Findings written up at [aifont.ai/blog/best-image-generation-model-for-text](https://aifont.ai/blog/best-image-generation-model-for-text).

# Text in AI images: did the model spell it right?

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.23815210.svg)](https://doi.org/10.5281/zenodo.23815210)

Every image we generate for **[aifont.ai](https://aifont.ai)** is checked letter by letter against the word the prompt asked for. This repo is that ledger: one record per image, with the prompt, the model, the word it was meant to write, and whether it wrote it.

<!-- stats -->
```
 2,495   images, each with a text verdict
    66   lettering styles
     8   languages
     5   image models
   733   also reviewed by a person
    74   disputed, waiting for a native speaker
```

Correct / judged. First attempts with the current prompt of each published style, so every model got the same prompts:

| Language | GPT Image | Nano Banana | Grok Imagine | Seedream | Qwen-Image |
|---|---:|---:|---:|---:|---:|
| English | 58/59 | 58/58 | 58/58 | 58/58 | 57/58 |
| German | 59/59 | 59/59 | 58/59 | 56/59 | 57/59 |
| Spanish | 59/59 | 59/59 | 58/59 | 57/59 | 49/59 |
| Portuguese | 59/59 | 59/59 | 55/59 | 59/59 | 50/59 |
| Chinese (Simplified) | 59/59 | 58/59 | 58/59 | 56/59 | 56/59 |
| Japanese | 59/59 | 58/59 | 52/59 | 51/59 | 45/59 |
| Arabic | 48/50 | 51/51 | 35/49 | 27/47 | 6/53 |
<!-- /stats -->

## What a record looks like

```json
{
  "id": 2172,
  "style": "witch-font",
  "language": "en",
  "target_text": "Potion",
  "model": "Qwen-Image",
  "model_id": "qwen-image-3.0-pro",
  "generated_at": "2026-10-28T17:05:33Z",
  "attempt": "first",
  "image_url": "https://cos.aifont.ai/research/attempts/witch-font/witch-font-en-qwen3pro-s1-92438.jpg",
  "gallery_id": 92438,
  "text_verdict": "correct",
  "error_types": [],
  "rendered_text": null,
  "reviews": [
    { "reviewer": "ai", "text_verdict": "correct", "native_speaker": false, "zoomed": false },
    { "reviewer": "maintainer", "text_verdict": "correct", "native_speaker": false, "zoomed": true }
  ],
  "human_reviewed": true,
  "disputed": false
}
```

Field by field in [SCHEMA.md](SCHEMA.md). The prompt text is in `prompts.jsonl`, keyed by `prompt_hash`.

## How the images were made

- Each lettering style has one prompt per language, written in that language, with a word in that language. The same prompt goes unchanged to every model.
- Five models, called through the same service, at one fixed resolution and quality per model. `model_id` is the upstream model version at the moment of generation, read from the service's configuration, so a silent model swap shows up as a new id.
- Nothing is edited, retouched or cherry-picked. Failed images stay in the ledger. When an image was wrong we usually generated it once more; that second image is marked `"attempt": "retry"`.

## How the text was judged

- `correct` means every letter, stroke, dot and diacritic matches `target_text`. Upper or lower case alone does not make it wrong unless the prompt asked for one.
- `incorrect` records what kind of mistake it was (`error_types`) and, for spelling mistakes, what the image actually says (`rendered_text`).
- Every image was first judged by an AI assistant. A person then went through the ones most likely to be misjudged: every image the AI marked wrong or unsure, every Arabic image, CJK images the AI had only seen on a contact sheet, plus a random sample of the rest. The sample of 50 matched the AI on all 50, so the other AI-only verdicts are kept, with `human_reviewed: false`.
- That person was not a native speaker of most of these languages. Where the AI and the person disagree on Arabic, neither is a native reader, so the record is `unsure` with `disputed: true`. Those are left out of the accuracy table.
- `reviews` lists each reviewer's own call, so you can weigh them yourself. This is not a 100% verified ground truth, and it isn't presented as one.

## Help fix it

If you read one of these languages and an image is judged wrong, open an issue with the record `id` and what the image actually says ("Wrong verdict" template). Native speakers are what this dataset needs most, Arabic first. Accepted corrections are added as a new review under your GitHub handle, and the next release recomputes the verdict.

## Counting accuracy

Use first attempts only (`"attempt": "first"`). Retries exist because the first try failed, and extra samples were drawn after a correct one, so both lean toward correct. To compare models, also keep to the published styles (`"purpose": "published"`) and the prompt version each style uses now: new styles were drafted on one model before all five ran. `summary.json` and the table above are filtered that way, and leave out disputed images. With about 60 images per cell, report counts, not percentages.

## Files

| File | |
|---|---|
| `generations.jsonl` | One record per image |
| `generations.csv` | Same, flattened, without prompts |
| `prompts.jsonl` | Prompt text by hash |
| `summary.json` | Correct / incorrect by model × language, first attempts |

Images are linked, not included. `gallery_id` is the image's public page on FontVibe, the service that generated it.

## License

Data: [CC BY 4.0](LICENSE). Credit "AIFont (aifont.ai)". Code in `scripts/`: MIT. See [NOTICE](NOTICE).

## Citing

DOI: [10.5281/zenodo.23815210](https://doi.org/10.5281/zenodo.23815210) (all versions). See [CITATION.cff](CITATION.cff), or GitHub's "Cite this repository" button.

	---
	license: cc-by-4.0
	pretty_name: AIFont text rendering in AI images
	language:
	- en
	- de
	- es
	- pt
	- zh
	- ja
	- ar
	task_categories:
	- text-to-image
	- image-to-text
	tags:
	- text-rendering
	- typography
	- evaluation
	- multilingual
	- image-generation
	size_categories:
	- 1K<n<10K
	configs:
	- config_name: default
	data_files:
	- split: train
	path: generations.jsonl
	---

	> Mirror of [Funovate-Technology/aifont-text-rendering](https://github.com/Funovate-Technology/aifont-text-rendering) (canonical copy, DOI [10.5281/zenodo.23815210](https://doi.org/10.5281/zenodo.23815210)). Corrections go to the GitHub repo's issues. Findings written up at [aifont.ai/blog/best-image-generation-model-for-text](https://aifont.ai/blog/best-image-generation-model-for-text).

	# Text in AI images: did the model spell it right?

	[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.23815210.svg)](https://doi.org/10.5281/zenodo.23815210)

	Every image we generate for [aifont.ai](https://aifont.ai) is checked letter by letter against the word the prompt asked for. This repo is that ledger: one record per image, with the prompt, the model, the word it was meant to write, and whether it wrote it.

	<!-- stats -->
	```
	2,495 images, each with a text verdict
	66 lettering styles
	8 languages
	5 image models
	733 also reviewed by a person
	74 disputed, waiting for a native speaker
	```

	Correct / judged. First attempts with the current prompt of each published style, so every model got the same prompts:

	\| Language \| GPT Image \| Nano Banana \| Grok Imagine \| Seedream \| Qwen-Image \|
	\|---\|---:\|---:\|---:\|---:\|---:\|
	\| English \| 58/59 \| 58/58 \| 58/58 \| 58/58 \| 57/58 \|
	\| German \| 59/59 \| 59/59 \| 58/59 \| 56/59 \| 57/59 \|
	\| Spanish \| 59/59 \| 59/59 \| 58/59 \| 57/59 \| 49/59 \|
	\| Portuguese \| 59/59 \| 59/59 \| 55/59 \| 59/59 \| 50/59 \|
	\| Chinese (Simplified) \| 59/59 \| 58/59 \| 58/59 \| 56/59 \| 56/59 \|
	\| Japanese \| 59/59 \| 58/59 \| 52/59 \| 51/59 \| 45/59 \|
	\| Arabic \| 48/50 \| 51/51 \| 35/49 \| 27/47 \| 6/53 \|
	<!-- /stats -->

	## What a record looks like

	```json
	{
	"id": 2172,
	"style": "witch-font",
	"language": "en",
	"target_text": "Potion",
	"model": "Qwen-Image",
	"model_id": "qwen-image-3.0-pro",
	"generated_at": "2026-10-28T17:05:33Z",
	"attempt": "first",
	"image_url": "https://cos.aifont.ai/research/attempts/witch-font/witch-font-en-qwen3pro-s1-92438.jpg",
	"gallery_id": 92438,
	"text_verdict": "correct",
	"error_types": [],
	"rendered_text": null,
	"reviews": [
	{ "reviewer": "ai", "text_verdict": "correct", "native_speaker": false, "zoomed": false },
	{ "reviewer": "maintainer", "text_verdict": "correct", "native_speaker": false, "zoomed": true }
	],
	"human_reviewed": true,
	"disputed": false
	}
	```

	Field by field in [SCHEMA.md](SCHEMA.md). The prompt text is in `prompts.jsonl`, keyed by `prompt_hash`.

	## How the images were made

	- Each lettering style has one prompt per language, written in that language, with a word in that language. The same prompt goes unchanged to every model.
	- Five models, called through the same service, at one fixed resolution and quality per model. `model_id` is the upstream model version at the moment of generation, read from the service's configuration, so a silent model swap shows up as a new id.
	- Nothing is edited, retouched or cherry-picked. Failed images stay in the ledger. When an image was wrong we usually generated it once more; that second image is marked `"attempt": "retry"`.

	## How the text was judged

	- `correct` means every letter, stroke, dot and diacritic matches `target_text`. Upper or lower case alone does not make it wrong unless the prompt asked for one.
	- `incorrect` records what kind of mistake it was (`error_types`) and, for spelling mistakes, what the image actually says (`rendered_text`).
	- Every image was first judged by an AI assistant. A person then went through the ones most likely to be misjudged: every image the AI marked wrong or unsure, every Arabic image, CJK images the AI had only seen on a contact sheet, plus a random sample of the rest. The sample of 50 matched the AI on all 50, so the other AI-only verdicts are kept, with `human_reviewed: false`.
	- That person was not a native speaker of most of these languages. Where the AI and the person disagree on Arabic, neither is a native reader, so the record is `unsure` with `disputed: true`. Those are left out of the accuracy table.
	- `reviews` lists each reviewer's own call, so you can weigh them yourself. This is not a 100% verified ground truth, and it isn't presented as one.

	## Help fix it

	If you read one of these languages and an image is judged wrong, open an issue with the record `id` and what the image actually says ("Wrong verdict" template). Native speakers are what this dataset needs most, Arabic first. Accepted corrections are added as a new review under your GitHub handle, and the next release recomputes the verdict.

	## Counting accuracy

	Use first attempts only (`"attempt": "first"`). Retries exist because the first try failed, and extra samples were drawn after a correct one, so both lean toward correct. To compare models, also keep to the published styles (`"purpose": "published"`) and the prompt version each style uses now: new styles were drafted on one model before all five ran. `summary.json` and the table above are filtered that way, and leave out disputed images. With about 60 images per cell, report counts, not percentages.

	## Files

	\| File \| \|
	\|---\|---\|
	\| `generations.jsonl` \| One record per image \|
	\| `generations.csv` \| Same, flattened, without prompts \|
	\| `prompts.jsonl` \| Prompt text by hash \|
	\| `summary.json` \| Correct / incorrect by model × language, first attempts \|

	Images are linked, not included. `gallery_id` is the image's public page on FontVibe, the service that generated it.

	## License

	Data: [CC BY 4.0](LICENSE). Credit "AIFont (aifont.ai)". Code in `scripts/`: MIT. See [NOTICE](NOTICE).

	## Citing

	DOI: [10.5281/zenodo.23815210](https://doi.org/10.5281/zenodo.23815210) (all versions). See [CITATION.cff](CITATION.cff), or GitHub's "Cite this repository" button.