# Document AI: Benchmarks, Models and Applications

**Authors:** Lei Cui, Yiheng Xu, Tengchao Lv, Furu Wei  
**arXiv:** [2111.08609](https://arxiv.org/abs/2111.08609)  
**Date:** 2021-11-16  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2111.08609v1/

## Key points

- Document AI, or Document Intelligence, is a research field focused on automating the reading, understanding, and analysis of business documents.
- The recent surge in deep learning technologies has propelled the evolution of Document AI by enabling tasks such as document layout analysis, visual information extraction, document visual question answering, and document image classification to be tackled with greater efficiency.
- In their paper titled "Document AI: Benchmarks, Models and Applications," authors Lei Cui, Yiheng Xu, Tengchao Lv, and Furu Wei provide an overview of representative models, tasks, and benchmark datasets within the realm of Document AI.
- The authors explore potential directions for further advancements in Document AI research and highlight current state-of-the-art techniques for researchers and practitioners interested in leveraging Document AI for enhanced document processing capabilities.

## Summary

Document AI, also known as Document Intelligence, is a burgeoning research field focused on automating the reading, understanding, and analysis of business documents. This area of study holds significant importance for advancements in natural language processing and computer vision. The recent surge in deep learning technologies has propelled the evolution of Document AI by enabling tasks such as document layout analysis, visual information extraction, document visual question answering, and document image classification to be tackled with greater efficiency. In their paper titled "Document AI: Benchmarks, Models and Applications," authors Lei Cui, Yiheng Xu, Tengchao Lv, and Furu Wei provide an insightful overview of representative models, tasks, and benchmark datasets within the realm of Document AI. They delve into various approaches including early-stage heuristic rule-based document analysis, statistical machine learning algorithms, and deep learning methodologies with a specific focus on pre-training methods. Looking towards the future of Document AI research , the authors explore potential directions for further advancements in this field. By shedding light on the current state-of-the-art techniques , this paper serves as a valuable resource for researchers and practitioners interested in leveraging Document AI for enhanced document processing capabilities. Overall, Document AI continues to be a dynamic area of research. As technology continues to evolve rapidly in this domain, to harness the full potential of Document Intelligence in real-world scenarios.