Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
Impact in
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- Authors
- Chen‐Yu LeeSimon Osindero
In The Last Decade
doi.org/10.1109/cvpr.2016.245 →Countries where authors are citing Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
This map shows the geographic impact of Recursive Recurrent Nets with Attention Modeling for OCR in the Wild. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Recursive Recurrent Nets with Attention Modeling for OCR in the Wild with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Recursive Recurrent Nets with Attention Modeling for OCR in the Wild more than expected).
Fields of papers citing Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
This network shows the impact of Recursive Recurrent Nets with Attention Modeling for OCR in the Wild. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Recursive Recurrent Nets with Attention Modeling for OCR in the Wild.
About Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
This paper, published in 2016, received 349 indexed citations . Written by Chen‐Yu Lee and Simon Osindero covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (331 citations), Artificial Intelligence (90 citations), Media Technology (89 citations), Signal Processing (15 citations) and Human-Computer Interaction (14 citations).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/cvpr.2016.245.