Datasets:

issai
/

DataFontID

license: cc-by-nc-4.0
task_categories:
  - image-classification
language:
  - en
  - fr
  - es
  - tr
  - az
  - uz
  - ru
  - kk
  - ky
  - ar
  - zh
size_categories:
  - 1M<n<10M
tags:
  - document-analysis
  - font-recognition
  - script-identification
  - typography
models:
  - issai/FontID
datasets:
  - issai/kazparc

DataFontID

A stratified synthetic corpus of 1,199,562 zero-margin text strips for multi-attribute typographic recognition. Every image carries four labels: font family, language, text color, and typographic style.


Images	1,199,562 (1,079,605 train / 59,978 val / 59,979 test)
Font families	75
Languages	11 across Latin, Cyrillic, Arabic, and Han scripts
Colors	64 (EGA palette)
Styles	Regular, Bold, Italic, Bold-Italic
Backgrounds	Texture, Noise, Complex, Document, Solid
Geometry	Zero-margin strips matching OCR extraction, variable aspect ratio

Existing font corpora render text onto fixed square canvases. OCR engines emit tightly cropped strips with variable aspect ratios, so models trained on square canvases face a domain shift at inference. DataFontID renders into strips bounded by precise font metrics, matching the geometry produced at extraction time.

Splits

The split is defined on the source pools before rendering, so no sentence and no background image seen during training appears in either evaluation split.

Related resources

Model: issai/FontID
Real-world benchmark: issai/Wild1024

Licensing

The DataFontID annotations, rendering pipeline, and synthesized text are released under CC BY-NC 4.0. Background imagery for the Texture, Noise, and Complex categories derives from the Describable Textures Dataset, which its authors make available to the computer vision community for research purposes; users should observe those terms for the background content. Text is drawn from KazParC and CulturaX. The corpus contains rendered raster images only and no font binaries.

Citation

Paper is coming soon.