readme.md

# Hindi (Devanagari) Text Rendering — Pilot Dataset

Synthetic image/text pairs for training a diffusion model to render **Hindi text
in Devanagari script**. 4,000 samples at 512x512, with typographically correct
shaping, exact ground-truth transcriptions, and per-sample glyph masks.

Built as a feasibility pilot for a Stable Diffusion 1.5 LoRA. Sized to answer one
question cheaply — *can the model learn Devanagari glyph shapes at all?* — rather
than to train a production model.

---

## Why this exists

Most text-rendering datasets are Latin-only, and most Devanagari datasets are
built for **recognition** (OCR), not **generation**. Those are not
interchangeable: recognition sets are deliberately adversarial — blurred,
occluded, low-resolution — and a generative model trained on them learns to
produce blurry, occluded text.

This dataset is the opposite: clean, sharp, and constrained so that every sample
is something a generator should aim to reproduce.

## What makes Devanagari hard

- **Shirorekha** — a continuous horizontal bar joins letters across a word.
  Models trained on Latin produce broken or wavy bars.
- **Conjuncts** — क् + ष → क्ष. Hundreds of ligatures whose shapes are not
  compositional from their parts.
- **Reordering** — the matra ि is written *before* the consonant it follows
  logically.
- **Three vertical zones** — matras sit above and below the base line, so glyph
  extent is far taller than Latin.
- **Fine diacritics** — nukta ़, anusvara ं, chandrabindu ँ are 2–8 pixel
  features that vanish under aggressive downsampling.

## Contents

```
data/pilot/
  images/          4,000 x 512x512 JPEG (quality 95)
  masks/           4,000 x 512x512 PNG, 8-bit glyph coverage masks
  metadata.jsonl   one record per sample
  train_words.txt  72 words used in the images
  holdout_words.txt 20 words deliberately NOT rendered
fonts/             22 OFL Devanagari fonts
corpus/            larger word lists (see Provenance)
src/               the generator, so results are reproducible
```

### `metadata.jsonl` schema

| field | type | meaning |
|---|---|---|
| `file` | str | path relative to `data/pilot/` |
| `text` | str | the Hindi string rendered — exact ground truth |
| `caption` | str | training caption (varied template) |
| `tokens` | int | CLIP token count of the caption, template included |
| `font` | str | filename in `fonts/` |
| `em` | int | font em size in pixels |
| `x`, `y` | int | top-left of the text box |
| `w`, `h` | int | text box size in pixels |
| `glyphs` | int | shaped glyph count (≠ character count, due to ligatures) |

```json
{"file": "images/000000.jpg", "text": "रुकें",
 "caption": "Hindi text \"रुकें\" on a board, flat design", "tokens": 18,
 "font": "Khand-Regular.ttf", "em": 40, "x": 150, "y": 405,
 "w": 62, "h": 48, "glyphs": 3}
```

## Statistics

| | |
|---|---|
| samples | 4,000 |
| resolution | 512 x 512 |
| unique words | 72 train (+20 held out, never rendered) |
| fonts | 22, evenly sampled (156–211 samples each) |
| font em size | 32–120 px, mean 75.5 |
| text box | 44–454 px wide, 30–161 px tall |
| caption tokens | 11–29 of CLIP's 77 |
| glyphs per sample | 2–8 |

Word vocabulary spans four groups: simple no-conjunct words, real signage
vocabulary, conjunct-bearing words (क्ष ज्ञ त्र श्र द्व ह्म …), and diacritic
stress cases (nukta, anusvara, chandrabindu, visarga).

## How it was generated

1. **Shaping** — text is shaped with **HarfBuzz** (`uharfbuzz`) and rasterised
   per-glyph with **FreeType** (`freetype-py`).

   > This matters more than it sounds. Pillow's published wheels ship **without
   > Raqm**, so `ImageDraw.text()` renders Devanagari *unshaped*: conjuncts break
   > into separate letters and pre-base ि lands on the wrong side of its
   > consonant. The output looks plausible to a non-reader while being wrong.
   > Any dataset built that way teaches a model incorrect letterforms.

   Verified: क्ष / त्र / ज्ञ each collapse 3 codepoints → 1 glyph; ि reorders
   ahead of its consonant; स्त्री → 2 glyphs.

2. **Composition** — flat, gradient, or lightly textured backgrounds; randomised
   font, size, position and colour, with foreground/background luminance
   contrast **enforced** (≥ 0.35), not sampled and hoped for.

3. **Captions** — 10 templates × 12 surface nouns, with the quoted Hindi string
   as the single invariant anchor. The phrase "Hindi text" is dropped in ~20% of
   samples so a model does not become dependent on a trigger phrase.

## The 32px rule

**Every sample renders text at font em size ≥ 32px, asserted at generation
time.** This is not a stylistic choice. Stable Diffusion 1.5's 4-channel VAE
downsamples 8×, and measured round-trips show Devanagari diacritics are
destroyed below ~24px em and clean at 32px:

| font em size | VAE reconstruction |
|---|---|
| 12–20 px | destroyed — nuktas gone, conjuncts illegible |
| 24 px | marginal — nukta merges into the stroke |
| **32 px** | **clean — minimum safe size** |
| 48–64 px | near-perfect |

Note this is **em size**, not ink-box height: measured ink/em ratio varies
0.67–1.08 depending on which matras a word carries, so ink height is
content-dependent and unsuitable as a threshold.

Consequence: this dataset covers **headline- and signage-sized text**, roughly
1–4 words. It is not suitable for training small or body-sized text rendering on
a 4-channel VAE at 512px — that is a reconstruction limit no training run can
overcome.

## Intended use

- Training / fine-tuning diffusion models (LoRA or full) for Devanagari text
- Evaluating text-rendering quality, with exact transcriptions as ground truth
- Glyph-conditioned approaches (ControlNet, GlyphControl, AnyText) — masks are
  included for this
- The 20 held-out words test generalisation rather than memorisation

**Not suitable for:** OCR / scene-text recognition training (these are clean
synthetic renders, not photographs), or small-text rendering (see above).

## Limitations

- **Synthetic only.** No photographic backgrounds, perspective, occlusion or
  real-world lighting. A model trained solely on this will render clean text but
  may not composite convincingly into photographs.
- **Small vocabulary.** 72 words is a pilot scale, chosen to make the
  feasibility question cheap to answer. It is far too small to teach the script
  broadly.
- **Conjunct coverage is partial.** श्च has zero coverage in the rendered
  samples; a larger stratified corpus is included separately.
- **One script, one language.** Devanagari as used for Hindi. Marathi and Nepali
  share the script with minor differences and are not specifically covered.

## Provenance and licensing

Three components with different origins — worth separating:

1. **Images and masks** — generated by the included code from a hand-written word
   list (`corpus/pilot_words.txt`) using OFL fonts. No third-party data.
   Released under **CC BY 4.0**.

2. **Fonts** (`fonts/`) — 22 families from Google Fonts, all under the **SIL Open
   Font License 1.1**, which permits embedding and redistribution. Includes Noto
   Sans/Serif Devanagari, Mukta, Hind, Halant, Tiro Devanagari Hindi, Baloo 2,
   Kalam, Khand, Martel, Modak, Palanquin, Rozha One, Sahitya, Teko, Yatra One,
   Amita, Biryani, Poppins. All audited for full Devanagari coverage and genuine
   conjunct formation.

3. **`corpus/corpus.json`** — the larger 4,569-word stratified vocabulary is
   **derived from transcriptions in IIIT-IndicSTR-Word** (words only; no images
   from that dataset are included or redistributed here).


If you use the corpus component, cite the source dataset:

```bibtex
@inproceedings{mondal2024indic,
  title={Indic Scene Text on the Roadside},
  author={Mondal, Ajoy and Tulsyan, Krishna and Jawahar, CV},
  booktitle={International Conference on Document Analysis and Recognition},
  pages={263--278}, year={2024}, organization={Springer}
}
```