mlsanityData to 7 Oct 2026

OmniPrint: A Configurable Printed Character Synthesizer

arXiv 2201.06648 ↗First posted 17 Jan 2022cs.CV cs.LGcited by 5

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Abstract (arXiv)

We introduce OmniPrint, a synthetic data generator of isolated printed characters, geared toward machine learning research. It draws inspiration from famous datasets such as MNIST, SVHN and Omniglot, but offers the capability of generating a wide variety of printed characters from various languages, fonts and styles, with customized distortions. We include 935 fonts from 27 scripts and many types of distortions. As a proof of concept, we show various use cases, including an example of meta-learning dataset designed for the upcoming MetaDL NeurIPS 2021 competition. OmniPrint is available at https://github.com/SunHaozhe/OmniPrint.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 44 of its 98 references that are in the map. First posted on arXiv 17 Jan 2022.

Builds on

44 of 98 references.

References · 44

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Cited by year

Citing papers per year: 2022: 2; 2023: 3.

5 papers.

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  2. 2022Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classificationcited by 10
  3. 2023Meta Omnium: A Benchmark for General-Purpose Learning-to-Learncited by 4
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