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
606
Longformer: The Long-Document Transformer
arXiv (Cornell University) · 2020 · 2.2K citations · Full text
Convolutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier et al. · arXiv (Cornell University) · 2017 · 1.9K citations · Full text
Convolutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier et al. · International Conference on Machine Learning · 2017 · 1.3K citations
1–5 of 606
2K
11
h-index
11
h-index
11
h-index
10
h-index
10
h-index
1–5 of 2K
505
Google (United States)
Mountain View, United States
20 h-index
125 publications
11.1K citations

Chinese Academy of Sciences
Beijing, China
20 h-index
68 publications
6.9K citations
University of Chinese Academy of Sciences
Beijing, China
14 h-index
44 publications
5.5K citations
Meta (Israel)
Tel Aviv, Israel
11 h-index
45 publications
20.6K citations

Johns Hopkins University
Baltimore, United States
11 h-index
33 publications
5K citations
Microsoft Research Asia (China)
Beijing, China
10 h-index
25 publications
1.3K citations
Institute of Automation
Beijing, China
10 h-index
33 publications
4.6K citations
Microsoft (United States)
Redmond, United States
10 h-index
25 publications
1.5K citations
1–8 of 505
Repository · 97 publications · 9K citations
39
h-index
12 publications · 895 citations
12
h-index
Conference · 12 publications · 699 citations
12
h-index
9 publications · 205 citations
9
h-index
TASLP · 8 publications · 334 citations
8
h-index
1–5