论文标题
用于历史文档的基于变压器的HTR
Transformer-based HTR for Historical Documents
论文作者
论文摘要
我们将Trocr框架应用于现实世界，历史手稿，并表明Trocr本身是一个强大的模型，是转移学习的理想选择。 Trocr仅接受了英语的培训，但它可以适应其他语言，这些语言很容易且很少有培训材料。我们将Trocr与SOTA HTR框架（Transkribus）进行比较，并表明它可以击败此类系统。这一发现是必不可少的，因为当Transkribus可以访问基线信息时，它的表现最佳，这根本不需要微调Trocr。
We apply the TrOCR framework to real-world, historical manuscripts and show that TrOCR per se is a strong model, ideal for transfer learning. TrOCR has been trained on English only, but it can adapt to other languages that use the Latin alphabet fairly easily and with little training material. We compare TrOCR against a SOTA HTR framework (Transkribus) and show that it can beat such systems. This finding is essential since Transkribus performs best when it has access to baseline information, which is not needed at all to fine-tune TrOCR.
