Sequence-to-sequence Recognition

Sequence-to-Sequence Recognition is a machine learning framework within optical character recognition that models the transcription of variable-length input sequences, such as images of text lines, into corresponding output sequences of characters or words by leveraging an encoder-decoder architecture with attention mechanisms.

606

Publications

49.2K

Citations

2K

Authors

505

Institutions

Publications per year

2017–2026

465

Variants

Seq2seq Recognition

Authors

2K

  1. SW
    Shinji Watanabe

    Johns Hopkins University, United States

    11 publications · 1.3K citations

    11

    h-index

  2. RP
    Rohit Prabhavalkar

    Google (United States), United States

    12 publications · 1.3K citations

    11

    h-index

  3. NJ
    Navdeep Jaitly

    Pacific Northwest National Laboratory, United States

    12 publications · 1.9K citations

    11

    h-index

  4. TN
    Tara N. Sainath

    Google (United States), United States

    10 publications · 1K citations

    10

    h-index

  5. YW
    Yonghui Wu

    Google (United States), United States

    10 publications · 948 citations

    10

    h-index

1–5 of 2K

Venues

  1. arXiv (Cornell University)

    Repository · 97 publications · 9K citations

    39

    h-index

  2. Pattern Recognition

    12 publications · 895 citations

    12

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 12 publications · 699 citations

    12

    h-index

  4. IEEE Access

    9 publications · 205 citations

    9

    h-index

  5. 8

    h-index

1–5