dots.ocr: 一种单一视觉-语言模型中的多语言文档布局解析
dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model
AI总结：
dots_ocr是一种单一视觉-语言模型，通过联合学习多语言文档布局解析的三个核心任务，实现了统一框架下的高效性能提升。
摘要：
文档布局解析是人工智能（AI）访问和解读世界庞大结构化知识库的关键入口。这一过程，包括布局检测、文本识别和关系理解，对于赋能下一代视觉-语言模型尤为重要。然而，当前的方法依赖于碎片化、多阶段的流水线，导致错误传播，并未能利用联合训练的协同效应。在本文中，我们介绍了dots_ocr，这是一种单一的视觉-语言模型，首次在统一的端到端框架中联合学习三个核心任务，展示了联合学习的优势。这得益于一个高度可扩展的数据引擎，能够合成庞大的多语言语料库，使模型能够在广泛的任务中提供稳健的性能，涵盖多种语言、布局和领域。我们统一范式的有效性通过在全面的OmniDocBench上达到最先进的性能得到验证。此外，为了促进全球文档智能的研究，我们引入了XDocParse，一个涵盖126种语言的具有挑战性的新基准。在该基准上，dots_ocr实现了最先进的性能，取得了约10%的相对提升，展示了强大的多语言能力。
英文摘要：
Document Layout Parsing serves as a critical gateway for Artificial Intelligence (AI) to access and interpret the world's vast stores of structured knowledge. This process,which encompasses layout detection, text recognition, and relational understanding, is particularly crucial for empowering next-generation Vision-Language Models. Current methods, however, rely on fragmented, multi-stage pipelines that suffer from error propagation and fail to leverage the synergies of joint training. In this paper, we introduce dots_ocr, a single Vision-Language Model that, for the first time, demonstrates the advantages of jointly learning 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, empowering the model to deliver robust performance across a wide array of tasks, encompassing diverse languages, layouts, and domains. The efficacy of our unified paradigm is validated by state-of-the-art performance on the comprehensive OmniDocBench. Furthermore, to catalyze research in global document intelligence, we introduce XDocParse, a challenging new benchmark spanning 126 languages. On this benchmark, dots_ocr achieves state-of-the-art performance, delivering an approximately 10% relative improvement and demonstrating strong multilingual capability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
