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Unified Structure Learning for OCR-free Document Understanding
Introduction
Researchers from Alibaba Group and the Renmin University of China have developed DocOwl 1.5, a Unified Structure Learning system, to enhance the performance of Multimodal Large Language Models (MLLMs) in understanding text-rich images.
Key Components
- H-Reducer: A vision-to-text module designed to maintain rich text information during vision-and-language feature alignment.
- Unified Structure Learning: Comprising structure-aware parsing tasks and multi-grained text localization tasks across five domains: document, webpage, table, chart, and natural image. It helps MLLMs understand text-rich images more efficiently.
- Two-stage Training: Enhances basic text recognition and structure parsing abilities, making the model more efficient for downstream document understanding.
Performance
DocOwl 1.5 outperforms other models on ten visual document understanding benchmarks, showcasing state-of-the-art OCR-free performance.
Practical AI Solutions
For companies looking to evolve with AI, leveraging solutions like DocOwl 1.5 can redefine their way of work. Identifying automation opportunities, defining KPIs, selecting AI solutions, and implementing gradually are key steps in this process.
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Contact Us
For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com. Stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom for more updates.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Researchers from Alibaba and the Renmin University of China Present mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding
- MarkTechPost
- Twitter – @itinaicom
