Noisy Image Recognition

Noisy Image Recognition is a computational framework within optical character recognition that specifically addresses the challenge of accurately identifying text in images degraded by noise, distinguishing itself from general text recognition methods by its focus on preprocessing, robust feature extraction, and noise-invariant modeling to restore or interpret text in low-quality visual inputs.

310

Publications

20.9K

Citations

888

Authors

361

Institutions

Publications per year

2017–2026

103

Authors

888

  1. DD
    David Doermann

    University of Maryland, College Park, United States

    7 publications · 1.1K citations

    7

    h-index

  2. HL
    Huiping Li

    University of Maryland, College Park, United States

    4 publications · 245 citations

    4

    h-index

  3. VG
    Venu Govindaraju

    University at Buffalo, State University of New York, United States

    4 publications · 60 citations

    4

    h-index

  4. HS
    Huazhong Shu

    Southeast University, China

    3 publications · 171 citations

    3

    h-index

  5. VK
    Vitaly Kober

    Centro de Investigación Científica y de Educación Superior de Ensenada, Mexico

    3 publications · 60 citations

    3

    h-index

1–5 of 888

Venues

  1. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 11 publications · 1.6K citations

    11

    h-index

  2. Pattern Recognition

    9 publications · 978 citations

    9

    h-index

  3. IEEE Transactions on Image Processing

    7 publications · 451 citations

    7

    h-index

  4. arXiv (Cornell University)

    Repository · 7 publications · 174 citations

    7

    h-index

  5. IEEE Access

    6 publications · 153 citations

    6

    h-index

1–5