dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model
Summary
This paper presents dots.ocr, a unified Vision-Language Model that jointly learns layout detection, text recognition, and relational understanding for multilingual document layout parsing. It achieves state-of-the-art results on OmniDocBench and introduces the XDocParse benchmark spanning 126 languages.
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Paper page - dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model
Source: https://huggingface.co/papers/2512.02498
Abstract
A unified Vision-Language Model, dots.ocr, achieves state-of-the-art performance on document layout parsing by jointly learning layout detection, text recognition, and relational understanding, validated on OmniDocBench and XDocParse benchmarks.
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 encompasseslayout detection,text recognition, and relational understanding, is particularly crucial for empowering next-generationVision-Language Models. Current methods, however, rely on fragmented, multi-stage pipelines that suffer from error propagation and fail to leverage the synergies ofjoint training. In this paper, we introduce dots.ocr, a singleVision-Language Modelthat, 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 comprehensiveOmniDocBench. Furthermore, to catalyze research in global document intelligence, we introduceXDocParse, a challenging new benchmark spanning 126 languages. On this testbed, dots.ocr establishes a powerful new baseline, outperforming the next-best competitor by a remarkable +7.4 point margin and proving its unparalleled multilingual capabilities.
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