Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Document AI: Benchmarks, Models and Applications   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.

20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th