Understanding document images (e.g., invoices)  is a core but challenging task since it requires complex functions such asreading textand aholistic understanding of the document.
Current Visual Document Understanding (VDU)  methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR)  engines and focus on the understanding task with the OCR outputs.
Although such OCR-based approaches have shown promising performance, they suffer from 1)  high computational costs for using OCR; 2)  inflexibility of OCR models on languages or types of documents; 3)  OCR error propagation to the subsequent process.
To address these issues, in this paper, we introduce a novel OCR-free VDU model namedDonut, which stands forDocumentunderstandingtransformer.
As the first step in OCR-free VDU research, we propose a simple architecture (i.e., Transformer)  with a pre-training objective (i.e.,cross-entropy loss) .
Donut is conceptually simple yet effective. Through extensive experiments and analyses, we show a simple OCR-free VDU model,Donut, achieves state-of-the-art performances on various VDU tasks in terms of both speed and accuracy.
In addition, we offer a synthetic data generator that helps the model pre-training to be flexible in various languages and domains.
The code, trained model, and synthetic data are available athttps://github.com/clovaai/donut.