💡 The paper introduces dots.ocr, a unified Vision-Language Model that achieves state-of-the-art performance on document layout parsing by jointly learning layout detection, text recognition, and relational understanding. The current methods for document layout parsing rely on fragmented, multi-stage pipelines that suffer from error propagation and fail to leverage the synergies of joint training. The proposed model addresses this issue by using a single Vision-Language Model that jointly learns the three core tasks within a unified, end-to-end framework. This is made possible by a highly scalable data engine that synthesizes a vast multilingual corpus, enabling the model to deliver robust performance across a wide array of tasks, languages, layouts, and domains. The model is validated on the OmniDocBench and XDocParse benchmarks, with the latter being a new challenging benchmark introduced in the paper that spans 126 languages. The results show that dots.ocr establishes a powerful new baseline, outperforming the next-best competitor by a 7.4 point margin and proving its unparalleled multilingual capabilities. The paper's contributions include the introduction of a unified Vision-Language Model that achieves state-of-the-art performance on document layout parsing, the creation of a new benchmark for multilingual document intelligence, and the demonstration of the advantages of jointly learning layout detection, text recognition, and relational understanding within a single model.