Nafnlaus/galdrastafir-fonts

Galdrastafir Font Recognition Dataset

Synthetic font-in-the-wild images generated with ControlNet + diffusion for training font recognition models. Each image has per-layer metadata (most with bounding boxes) for every font rendered into the scene. Fonts appear as signs, sculptures, paintings, fashion, screens, graffiti, and more — rendered in 3D with diverse transformations.

Columns

column	type	description
image_id	str	unique image identifier (e.g. 003034722_00001)
json_id	str	ID of the source JSON metadata file
image_filename	str	original filename (e.g. 003034722_00001.webp)
source_directory	str	name of the source image batch (e.g. font_images_out_4673)
image	Image	the image (WebP format, usable directly as PIL)
image_format	str	actual image format sniffed from bytes (webp for 99.999%)
json_content	str	full JSON metadata string (see schema below)
num_layers	int	number of font layers in this image

Loading Images

The image column is stored as an embedded HuggingFace Image struct and loads automatically as a PIL image:

from datasets import load_dataset, Image as HFImage

ds = load_dataset("Nafnlaus/galdrastafir-fonts", split="train", streaming=True)
# Cast if needed for auto-PIL decoding
ds = ds.cast_column("image", HFImage())

for sample in ds:
    img  = sample["image"]           # PIL.Image.Image  (no io.BytesIO needed)
    meta = json.loads(sample["json_content"])
    for layer in meta.get("layers", []):
        print(layer["font_name"], layer.get("coordinates"))
    break

Or manually if you prefer:

import io, json
from PIL import Image
from datasets import load_dataset

ds = load_dataset("Nafnlaus/galdrastafir-fonts", split="train", streaming=True)
for sample in ds:
    img  = Image.open(io.BytesIO(sample["image"]["bytes"]))
    meta = json.loads(sample["json_content"])
    break

JSON Metadata Schema

Each image has 1–8 font layers. The json_content field is a JSON string:

{
  "id": 3034722,
  "num_layers": 2,
  "layers": [
    {
      "font_name": "Wet Paint",
      "font_filename": "Wetpaitt.ttf",
      "text": "\"Everyone!\"",
      "font_size": 768,
      "rotation": [-1.6, -3.62, -35.62],
      "style": {
        "weight": "regular",
        "style": "italic",
        "underline": true,
        "strikeout": false,
        "alignment": "right",
        "char_spacing_px": 0.0,
        "line_spacing_px": 0.0,
        "weight_value": 80.0,
        "slant_value": 0.0,
        "is_variable": false,
        "variation_index": 0
      },
      "coordinates": {"left": 4, "right": 307, "top": 597, "bottom": 699},
      "center_point": [155, 648]
    }
  ]
}

Note: coordinates and center_point are present in ~43% of images; the rest have the text placed but without a recorded bounding box.

Scale & Validation

• 1,523,517 images, 153 parquet shards (~700 MB each)
• ~314,000 unique font names across the full dataset
• All images are WebP (9 rows, 0.0006%, have corrupt/missing bytes)
• 100% valid JSON metadata
• Total uncompressed: ~108 GB

Notes

• Some images are NSFW (the diffusion model was given creative freedom).
• Bounding-box data is absent in some images (older generation batches).
• Many fonts may have missing glyphs that render as blank/squares; filter by metadata if this is a problem for your use case.
• Images were generated using FLUX.1 schnell as the diffusion backbone.