Geewook Kim
Impact in
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- Handwritten Text Recognition Techniques
- Image Processing and 3D Reconstruction
- Image Retrieval and Classification Techniques
- Advanced Image and Video Retrieval Techniques
- Multimodal Machine Learning Applications
- Media Technology top 5%
- Vehicle License Plate Recognition
Papers in
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- Topic Modeling 6
- Natural Language Processing Techniques 5
- Domain Adaptation and Few-Shot Learning 3
- Advanced Graph Neural Networks 2
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- Handwritten Text Recognition Techniques 3
- Multimodal Machine Learning Applications 3
- Image Processing and 3D Reconstruction 1
- Advanced Image and Video Retrieval Techniques 1
- Co-authors
- Sangdoo Yun (2 shared papers)Junyeop Lee (2 shared papers)Jeonghun Baek (1 shared paper)Seong Joon Oh (1 shared paper)Hwalsuk Lee (2 shared papers)Dongyoon Han (1 shared paper)Sungrae Park (2 shared papers)Hidetoshi Shimodaira (4 shared papers)
- Journals
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- South KoreaJapanCanada
In The Last Decade
Geewook Kim
8 papers receiving 311 citations
Geewook Kim's Hit Papers
Peers
Comparison fields: 5 of 47
- Computer Vision and Pattern Recognition 271
- Media Technology 80
- Computational Mathematics 2
- Artificial Intelligence 107
- Human-Computer Interaction 7
Countries citing papers authored by Geewook Kim
This map shows the geographic impact of Geewook Kim's research. 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 Geewook Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geewook Kim more than expected).
Fields of papers citing papers by Geewook Kim
This network shows the impact of papers produced by Geewook Kim. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Geewook Kim. The network helps show where Geewook Kim may publish in the future.
Co-authors
The 17 scholars most cited alongside Geewook Kim, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis Hit paper breakdown → | 2019 | 306 |
| 2 | 2021 | 7 | |
| 3 | 2019 | 4 | |
| 4 | 2024 | 4 | |
| 5 | 2018 | 4 | |
| 6 | 2018 | 3 | |
| 7 | 2019 | 2 | |
| 8 | 2023 | 1 | |
| 9 | 2020 | 0 | |
| 10 | 2023 | 0 |
About Geewook Kim
Geewook Kim is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Infectious Diseases, Organic Chemistry and Surgery, having authored 10 papers that have together received 331 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers), Domain Adaptation and Few-Shot Learning (3 papers), Handwritten Text Recognition Techniques (3 papers), Multimodal Machine Learning Applications (3 papers), Advanced Graph Neural Networks (2 papers), Image Processing and 3D Reconstruction (1 paper) and Advanced Image and Video Retrieval Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (271 citations), Media Technology (80 citations), Computational Mathematics (2 citations), Artificial Intelligence (107 citations) and Human-Computer Interaction (7 citations). Geewook Kim has collaborated with scholars based in South Korea, Japan and Canada. Frequent co-authors include Sangdoo Yun, Junyeop Lee, Jeonghun Baek, Seong Joon Oh, Hwalsuk Lee, Dongyoon Han, Sungrae Park, Hidetoshi Shimodaira, Minjoon Seo and Wonseok Hwang. Their work appears in journals such as Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing and arXiv (Cornell University).
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.