The reviewed record of science sign in
Pith

arxiv: 2111.08609 · v1 · pith:UXOZX24N · submitted 2021-11-16 · cs.CL

Document AI: Benchmarks, Models and Applications

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:UXOZX24Nrecord.jsonopen to challenge →

classification cs.CL
keywords documentlearningresearchanalysisdeepmodelsvisualadvanced
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. How Far Is Document Parsing from Solved? PureDocBench: A Source-TraceableBenchmark across Clean, Degraded, and Real-World Settings

    cs.CV 2026-05 conditional novelty 8.0

    PureDocBench shows document parsing is far from solved, with top models at ~74/100, small specialists competing with large VLMs, and ranking reversals under real degradation.

  2. The Documentation and Traceability Burden of the Indian EV Transition

    cs.CE 2026-07 conditional novelty 7.0

    The paper systematises India's EV compliance-document lifecycle into a two-layer evidence model, a six-stage lifecycle with four failure loci, an exergy-destruction analytic lens, and a six-problem research agenda.

  3. CC-OCR V2: Benchmarking Large Multimodal Models for Literacy in Real-world Document Processing

    cs.CL 2026-05 unverdicted novelty 6.0

    CC-OCR V2 reveals that state-of-the-art large multimodal models substantially underperform on challenging real-world document processing tasks.