Automated Document Processing

Automated Document Processing is a systematic computational method that transforms unstructured or semi-structured document inputs into structured, analyzable data through the application of machine learning, natural language processing, and pattern recognition techniques, distinguishing it from manual or rule-based document handling by its adaptive, scalable, and intelligent processing capabilities.

394

Publications

18.6K

Citations

1.1K

Authors

420

Institutions

Publications per year

2017–2026

87

Authors

1.1K

  1. CY
    Ching Y. Suen

    Concordia University, Canada

    10 publications · 380 citations

    10

    h-index

  2. KR
    Kaushik Roy

    West Bengal State University, India

    8 publications · 263 citations

    8

    h-index

  3. TP
    Thierry Paquet

    Université de Rouen Normandie, France

    8 publications · 172 citations

    8

    h-index

  4. FE
    Floriana Esposito

    University of Bari Aldo Moro, Italy

    7 publications · 125 citations

    7

    h-index

  5. AD
    Andreas Dengel

    German Research Centre for Artificial Intelligence, Germany

    6 publications · 217 citations

    6

    h-index

1–5 of 1.1K

Venues

  1. 7

    h-index

  2. arXiv (Cornell University)

    Repository · 7 publications · 156 citations

    7

    h-index

  3. 6

    h-index

  4. Pattern Recognition

    5 publications · 467 citations

    5

    h-index

  5. IEEE Access

    5 publications · 272 citations

    5

    h-index

1–5