mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding

Published on Mar 19
· Featured in Daily Papers on Mar 20


Structure information is critical for understanding the semantics of text-rich images, such as documents, tables, and charts. Existing Multimodal Large Language Models (MLLMs) for Visual Document Understanding are equipped with text recognition ability but lack general structure understanding abilities for text-rich document images. In this work, we emphasize the importance of structure information in Visual Document Understanding and propose the Unified Structure Learning to boost the performance of MLLMs. Our Unified Structure Learning comprises structure-aware parsing tasks and multi-grained text localization tasks across 5 domains: document, webpage, table, chart, and natural image. To better encode structure information, we design a simple and effective vision-to-text module H-Reducer, which can not only maintain the layout information but also reduce the length of visual features by merging horizontal adjacent patches through convolution, enabling the LLM to understand high-resolution images more efficiently. Furthermore, by constructing structure-aware text sequences and multi-grained pairs of texts and bounding boxes for publicly available text-rich images, we build a comprehensive training set DocStruct4M to support structure learning. Finally, we construct a small but high-quality reasoning tuning dataset DocReason25K to trigger the detailed explanation ability in the document domain. Our model DocOwl 1.5 achieves state-of-the-art performance on 10 visual document understanding benchmarks, improving the SOTA performance of MLLMs with a 7B LLM by more than 10 points in 5/10 benchmarks. Our codes, models, and datasets are publicly available at


Great work! Would love to see this evaluated on DUDE, a really challenging document VQA dataset. Happy to help ;)

Paper author

Thanks for your approval and suggestion, we will consider multi-page document understanding in our next version and DUDE will be a suitable dataset.

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Seems to be interesting.

Is it an open source project or proprietary? Do you share the model?


Paper author

mPLUG-DocOwl 1.5 is an open-source project. Data, models, and codes are scheduled to be shared at in next week.

Thanks Anweb Hu for your short notice answer.

Amazing, can’t wait to test and will give you feedback for sure.

Have a great day.


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