Document AI: Benchmarks, Models and Applications
원문 초록
Document AI, or Document Intelligence, is a relatively new research topic that refers to the techniques for automatically reading, understanding, and analyzing business documents. It is an important research direction for natural language processing and computer vision. In recent years, the popularity of deep learning technology has greatly advanced the development of Document AI, such as document layout analysis, visual information extraction, document visual question answering, document image classification, etc. This paper briefly reviews some of the representative models, tasks, and benchmark datasets. Furthermore, we also introduce early-stage heuristic rule-based document analysis, statistical machine learning algorithms, and deep learning approaches especially pre-training methods. Finally, we look into future directions for Document AI research.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models
SeqPE: Transformer with Sequential Position Encoding
Can We Infer Confidential Properties of Training Data from LLMs?
SUT: a new multi-purpose synthetic dataset for Farsi document image analysis
DocXClassifier: High Performance Explainable Deep Network for Document Image Classification
From Roots to Rewards: Dynamic Tree Reasoning with RL
토론
이 논문과 연결된 포럼 글이 아직 없습니다.