SCOB: Universal Text Understanding via Character-wise Supervised
  Contrastive Learning with Online Text Rendering for Bridging Domain Gap
Daehee Kim Yoonsik Kim DongHyun Kim Yumin Lim Geewook Kim Taeho Kil
Inspired by the great success of language model (LM)-based pre-training,
recent studies in visual document understanding have explored LM-based
pre-training methods for modeling text within document images. Among them,
pre-training that reads all text from an image has shown promise, but often
exhibits instability and even fails when applied to broader domains, such as
those involving both visual documents and scene text images. This is a
substantial limitation for real-world scenarios, where the processing of text
image inputs in diverse domains is essential. In this paper, we investigate
effective pre-training tasks in the broader domains and also propose a novel
pre-training method called SCOB that leverages character-wise supervised
contrastive learning with online text rendering to effectively pre-train
document and scene text domains by bridging the domain gap. Moreover, SCOB
enables weakly supervised learning, significantly reducing annotation costs.
Extensive benchmarks demonstrate that SCOB generally improves vanilla
pre-training methods and achieves comparable performance to state-of-the-art
methods. Our findings suggest that SCOB can be served generally and effectively
for read-type pre-training methods. The code will be available at
https://github.com/naver-ai/scob.