Nasser4963/jamo-gold

JAMO Gold — Human-Validated Hangul Rendering Judgments

A small, carefully labelled dataset for a specific purpose: testing whether an OCR engine can tell that a glyph is not a real character.

It is not a Hangul OCR training set, and not a model leaderboard.

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Why this exists

Benchmarks that measure visual text rendering score generated images with OCR. But OCR engines are closed-set classifiers — given an image, they must return some character from their inventory. They cannot say "this isn't a character at all".

Generative models fail in exactly that way. In this dataset, 21.7% of generated images are Hangul-shaped glyphs that cannot be typed — for example, a final consonant ㅁ drawn with an internal cross (making it 田). Three OCR engines each silently map 77–83% of these onto a valid syllable.

This dataset provides the human labels needed to measure that gap.

Full analysis: docs/TECHNICAL_NOTE.md.

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Quickstart

from datasets import load_dataset

# 300 real generations, human-validated labels + 3 OCR engines' readings (no images)
gold = load_dataset("Nasser4963/jamo-gold", "gold_pilot", split="train")

# 581 font renderings with programmatic perturbations, ground truth by construction (images included)
synth = load_dataset("Nasser4963/jamo-gold", "synthetic_malformed", split="train")

# Concealment rate: how often does a judge return SOME valid syllable
# for a glyph that, by construction, isn't one?
invalid = synth.filter(lambda r: not r["is_control"])
concealed = sum(r["easyocr_reading"] is not None for r in invalid)
print(f"{concealed}/{len(invalid)} = {concealed / len(invalid):.1%}")

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Dataset structure

gold_pilot — 300 generated images with human labels

Real outputs from a commercial text-to-image model accessed via a paid API, single Hangul syllable targets, 18-cell stratified design. We withhold the vendor and model identifier pending written clarification on redistribution terms (see docs/RELEASE.md); this is a disclosure-scope choice, not an attempt to obscure the source, and the released image_manifest.jsonl (prompts + hashes, no pixels) already documents everything needed to regenerate the set once terms are confirmed.

The raw images (image field) are not included in this split. The released data/gold_pilot/*.parquet (loads automatically via load_dataset, also powers the Dataset Viewer preview on this page) ships the label and judge fields only, plus image_manifest.jsonl for regeneration once terms are confirmed.

Field	Type	Description
image_id	string	Stable hash ID (no target leakage); joins to image_manifest.jsonl
target	string	The syllable the model was asked to draw
template_id	string	T1 / T2 (prompt template)
n_sample_idx	int	Sample index within the 18-cell design
prompt	string	Full prompt text sent to the model
human_valid	bool	Primary label. Is this a typeable syllable?
human_reading	string | null	Transcription; present only when human_valid
vowel_class	string	simple_V / complex_V
coda_class	string	no_T / simple_T / cluster_T
onset_group	string	simple_O / aspir_O / tense_O
clova_reading	string | null	Naver CLOVA General OCR output
easyocr_reading	string | null	EasyOCR output
easyocr_confidence	float	EasyOCR confidence, 0–1
paddleocr_reading	string | null	PaddleOCR (korean_PP-OCRv5) output
paddleocr_confidence	float	PaddleOCR confidence, 0–1

The 4-way subcategory (malformed / non_hangul / multi_syllable) is not in this table — per the reliability numbers below (α = 0.576), it is not reliable enough to ship as a column. Use human_valid.

synthetic_malformed — 581 controlled items, no generative model

Font renderings, programmatically perturbed. Ground truth is guaranteed by construction. Use this split to test a judge without depending on any generator. Unlike gold_pilot, images are included — SIL OFL font derivatives carry no redistribution restriction — so this split has full Dataset Viewer thumbnail previews.

Field	Type	Description
image	image	Rendered glyph, 1024×1024
char	string	Base syllable before perturbation
kind	string	control / add_stroke / remove_stroke
severity	string	none / mild / moderate / severe
is_control	bool	If true, the glyph is a real, unmodified syllable
font_name	string	OFL font used
tm_reading	string	template_match (closed-form) reading — always answers
tm_score	float	template_match similarity score
tm_matches_original	bool	Did template_match snap back to char?
easyocr_reading	string | null	EasyOCR output (open-form — can abstain)
easyocr_confidence	float	EasyOCR confidence, 0–1
easyocr_matches_original	bool	Did EasyOCR read char exactly?

is_control == False ⟹ the glyph is not a valid syllable. Any judge returning a confident valid-syllable reading on those items is concealing. Reproduction: python scripts/build_hf_dataset.py regenerates these images deterministically (fixed seed) from jamo_bench.synthetic_malformed, then asserts row-for-row alignment against the archived eval results before writing — see the script docstring for exact parameters.

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The labelling criterion

The primary label answers one question:

Can this glyph be typed on a standard keyboard? (Equivalently: is it one of the 11,172 precomposed Hangul syllables?)

This replaced a subjective "legible / illegible" criterion after a pilot showed the latter was applied inconsistently. It is binary, checkable, and reproducible.

Reliability (two independent annotators, 87-item overlap)

Judgment	Agreement
Binary validity	97.7% raw · Krippendorff's α = 0.942
Transcription, given both said valid	23/23 = 100%
4-way subcategory	α = 0.576

Use the binary label. The subcategories (malformed vs non_hangul) are provided for exploration but are not reliable — the boundary is a continuum, and 17 of 21 inter-annotator disagreements fall on it.

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Intended uses

✅ Testing whether a judge can abstain. Feed it synthetic_malformed; measure how often it returns a valid syllable on is_control == False items, and whether its confidence distinguishes them. (In our tests: it does not — Mann–Whitney p = 0.841.)

✅ Calibrating an OCR-based scorer. Compare your engine's readings to human_reading on gold_pilot to estimate your own measurement bias before publishing benchmark numbers.

✅ Replicating the concealment finding with a fourth engine.

Uses to avoid

❌ Training a Hangul OCR model. 300 images, heavily skewed toward rare syllables. It will not generalise.

❌ Ranking generative models. One generator, no comparison set. The generator here is an instance used to obtain realistic failures, not a subject of evaluation.

❌ Citing the generator's accuracy as a model capability measure. With n = 300 on a deliberately rare-syllable-weighted design, the number is not a general capability estimate.

❌ Using the 4-way subcategory as ground truth (α = 0.576).

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Known limitations

• Single generator, n = 300. The 21.7% invalid rate is measured on one model; we do not claim it generalises. The judge behaviour claim is supported by the generator-free synthetic_malformed split, which does generalise.
• One of the two annotators is the dataset author. The second is independent; all agreement statistics are between the two.
• Per-cell values are unreliable. ~12 images per 18-cell bucket. Only the three marginal axes (vowel, coda, onset) support inference.
• Observational for structure claims. "Complex codas are harder" is confounded with character frequency and stroke count; we make no causal claim. See §7 of the technical note.
• synthetic_malformed perturbations are synthetic, mirroring observed failure types but not sampled from a generator's true error distribution.

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Headline numbers

Recompute all of these with python scripts/verify_claims.py.

Finding	Value
Outputs that are not valid syllables	21.7% (65/300)
Concealment rate — CLOVA / EasyOCR / PaddleOCR	76.9% / 83.1% / 78.5%
Engines snapping to different characters	34/44 = 77.3%
Accuracy understatement (human − OCR)	+25.6 to +50.0pp
Complex-coda penalty exaggeration	2.3× – 4.0×
Controlled test: confidence distinguishes fabricated glyphs?	No (p = 0.841)

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Licensing

• Annotations, code, metadata: CC BY 4.0 / Apache 2.0
• synthetic_malformed images: derived from SIL OFL fonts (Noto Sans KR, Noto Serif KR, Pretendard); OFL notices included in fonts/
• gold_pilot images: generated by a commercial T2I API. Redistribution is subject to that provider's terms, which we have not yet cleared. The image field is therefore not included for any row in the public release; the repository ships the labels, all judge readings, and image_manifest.jsonl (prompts + hashes) so the set can be regenerated once terms are confirmed. Crops of three distinct images from this set appear across two illustrative figures in the technical note (not as dataset rows); see docs/RELEASE.md for the scope and licensing of that exception, made at the depositor's discretion pending the same clarification.
• If the images are released later, they will not be under CC BY 4.0. A provider terms review (2026-08-12, not legal advice) found clauses in the underlying service's terms restricting use of model outputs for training, fine-tuning, annotation, or development of models or algorithms — CC BY 4.0 does not exclude such use, so it is not an appropriate license for these images. If released, they will carry a restricted research/evaluation license: reproduction and evaluation permitted; training, fine-tuning, or algorithm development using the images prohibited; no removal of AI-generated content markers; no implied vendor endorsement. See docs/RELEASE.md for the full policy and docs/BYTEPLUS_INQUIRY_DRAFT.md for the pending clarification request.

Citation

@techreport{lim2026jamo,
  title  = {OCR-Based Scoring Conceals Rendering Failures in
            Text-to-Image Evaluation: Evidence from Hangul},
  author = {Lim, Taewoo},
  year   = {2026},
  type   = {Technical Note}
}