Ron Litman
Impact in
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- Handwritten Text Recognition Techniques
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Image Processing and 3D Reconstruction
- Image Retrieval and Classification Techniques
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- Vehicle License Plate Recognition
Papers in
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- Natural Language Processing Techniques 4
- Topic Modeling 3
- Domain Adaptation and Few-Shot Learning 2
- Semantic Web and Ontologies 1
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- Multimodal Machine Learning Applications 4
- Advanced Image and Video Retrieval Techniques 2
- Handwritten Text Recognition Techniques 2
- Co-authors
- R. Manmatha (2 shared papers)Oron Anschel (1 shared paper)Roee Litman (1 shared paper)Shahar Tsiper (2 shared papers)Srikar Appalaraju (1 shared paper)Ali Furkan Biten (1 shared paper)Alona Golts (3 shared papers)Xing Niu (1 shared paper)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- IsraelUnited States
In The Last Decade
Ron Litman
7 papers receiving 154 citations
Peers
Comparison fields: 5 of 18
- Computer Vision and Pattern Recognition 142
- Media Technology 22
- Artificial Intelligence 75
- Signal Processing 7
- Human-Computer Interaction 3
Countries citing papers authored by Ron Litman
This map shows the geographic impact of Ron Litman'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 Ron Litman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ron Litman more than expected).
Fields of papers citing papers by Ron Litman
This network shows the impact of papers produced by Ron Litman. 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 Ron Litman. The network helps show where Ron Litman may publish in the future.
Co-authors
The 12 scholars most cited alongside Ron Litman, 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 | 2020 | 87 | |
| 2 | 2022 | 47 | |
| 3 | 2023 | 16 | |
| 4 | 2023 | 8 | |
| 5 | 2024 | 4 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 2 | |
| 8 | 2025 | 0 |
About Ron Litman
Ron Litman is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Media Technology and Infectious Diseases, having authored 8 papers that have together received 167 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (4 papers), Multimodal Machine Learning Applications (4 papers), Topic Modeling (3 papers), Advanced Image and Video Retrieval Techniques (2 papers), Handwritten Text Recognition Techniques (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Vehicle License Plate Recognition (1 paper) and Semantic Web and Ontologies (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (142 citations), Media Technology (22 citations), Artificial Intelligence (75 citations), Signal Processing (7 citations) and Human-Computer Interaction (3 citations). Ron Litman has collaborated with scholars based in Israel and United States. Frequent co-authors include R. Manmatha, Oron Anschel, Roee Litman, Shahar Tsiper, Srikar Appalaraju, Ali Furkan Biten, Alona Golts, Xing Niu, Maria Nădejde and Huayang Li. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.