David Doermann
- Computer Vision and Pattern Recognition top 0.05%
- Handwritten Text Recognition Techniques 97
- Image Retrieval and Classification Techniques 67
- Advanced Image and Video Retrieval Techniques 63
- Image Processing and 3D Reconstruction 42
- Video Analysis and Summarization 40
- Image and Object Detection Techniques 22
- Advanced Neural Network Applications 21
- Media Technology top 0.05%
- Signal Processing top 1%
- Artificial Intelligence top 0.5%
- Natural Language Processing Techniques 20
- Human-Computer Interaction top 2%
David Doermann
251 papers receiving 8.7k citations
Hit Papers
Peers
Comparison fields: 5 of 150
- Computer Vision and Pattern Recognition 8.1k
- Media Technology 2.4k
- Signal Processing 672
- Artificial Intelligence 2.0k
- Human-Computer Interaction 216
Countries citing papers authored by David Doermann
This map shows the geographic impact of David Doermann'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 David Doermann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Doermann more than expected).
Fields of papers citing papers by David Doermann
This network shows the impact of papers produced by David Doermann. 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 David Doermann. The network helps show where David Doermann may publish in the future.
Co-authorship network
The 25 scholars most cited alongside David Doermann, 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 | 2024 | 0 | |
| 2 | Future of software development with generative AIbreakdown → | 2024 | 35 |
| 3 | 2024 | 1 | |
| 4 | 2023 | 3 | |
| 5 | 2023 | 6 | |
| 6 | 2023 | 5 | |
| 7 | 2023 | 2 | |
| 8 | 2022 | 26 | |
| 9 | 2022 | 7 | |
| 10 | 2022 | 36 | |
| 11 | 2021 | 8 | |
| 12 | 2021 | 89 | |
| 13 | 2020 | 14 | |
| 14 | 2016 | 9 | |
| 15 | Leveraging Statistical Transliteration for Dictionary-Based English-Bengali CLIR of OCR'd Text | 2012 | 5 |
| 16 | 2007 | 14 | |
| 17 | Shot Boundary Detection using Pixel-to-Neighbor Image Differences in Video | 2004 | 4 |
| 18 | Proceedings of the 3rd international conference on Mobile and ubiquitous multimedia | 2004 | 10 |
| 19 | Measuring Structural Similarity of Document Pages for Searching Document Image Databases. | 2003 | 1 |
| 20 | TREC-10 Experiments at University of Maryland CLIR and Video. | 2001 | 10 |
About David Doermann
David Doermann is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Artificial Intelligence, Signal Processing and Computer Graphics and Computer-Aided Design, having authored 261 papers that have together received 9.3k indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (97 papers), Image Retrieval and Classification Techniques (67 papers), Advanced Image and Video Retrieval Techniques (63 papers), Image Processing and 3D Reconstruction (42 papers), Video Analysis and Summarization (40 papers), Image and Object Detection Techniques (22 papers), Advanced Neural Network Applications (21 papers) and Natural Language Processing Techniques (20 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (8.1k citations), Media Technology (2.4k citations), Signal Processing (672 citations), Artificial Intelligence (2.0k citations) and Human-Computer Interaction (216 citations). David Doermann has collaborated with scholars based in United States, China and India. Frequent co-authors include Huiping Li, Le Kang, Peng Ye, Qixiang Ye, Yi Li, Daniel DeMenthon, Yefeng Zheng, Jian Liang, Peng Ye and Omid E. Kia. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal on Document Analysis and Recognition (IJDAR), International Journal of Computer Vision, IEEE Transactions on Image Processing and Computer Vision and Image Understanding.
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.