David Doermann

15.5k total citations · 5 hit papers
261 papers, 9.3k citations indexed

About

David Doermann is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, David Doermann has authored 261 papers receiving a total of 9.3k indexed citations (citations by other indexed papers that have themselves been cited), including 223 papers in Computer Vision and Pattern Recognition, 74 papers in Artificial Intelligence and 31 papers in Media Technology. Recurrent topics in David Doermann's work include Handwritten Text Recognition Techniques (97 papers), Image Retrieval and Classification Techniques (67 papers) and Advanced Image and Video Retrieval Techniques (63 papers). David Doermann is often cited by papers focused on Handwritten Text Recognition Techniques (97 papers), Image Retrieval and Classification Techniques (67 papers) and Advanced Image and Video Retrieval Techniques (63 papers). David Doermann collaborates with scholars based in United States, China and India. David Doermann's 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 and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Transactions on Medical Imaging.

In The Last Decade

David Doermann

251 papers receiving 8.7k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Convolutional Neural Networks for No-Reference Image Quality Assessment

2014 836 citations Peng Ye, David Doermann et al. profile →
Text Detection and Recognition in Imagery: A Survey

2014 516 citations Qixiang Ye, David Doermann profile →
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning

2019 358 citations Baochang Zhang, Qixiang Ye et al. profile →
Blind Image Quality Assessment Based on High Order Statistics Aggregation

2016 352 citations Peng Ye, David Doermann et al. profile →
Future of software development with generative AI

2024 35 citations J. Sauvola, Sasu Tarkoma et al. Automated Software Engineering profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
David Doermann United States	46	8.1k	2.4k	2.0k	672	373	261	9.3k
Yanwei Pang China	50	6.5k 0.8×	1.4k 0.6×	2.3k 1.1×	742 1.1×	460 1.2×	260	8.8k
Zheng-Jun Zha China	60	9.3k 1.1×	1.5k 0.6×	3.7k 1.9×	569 0.8×	391 1.0×	380	12.0k
Qi Tian China	57	9.7k 1.2×	1.2k 0.5×	3.1k 1.6×	736 1.1×	522 1.4×	426	12.0k
Alberto Del Bimbo Italy	47	8.4k 1.0×	912 0.4×	1.8k 0.9×	1.3k 1.9×	487 1.3×	532	9.9k
Ling‐Yu Duan China	47	6.8k 0.8×	747 0.3×	2.0k 1.0×	836 1.2×	384 1.0×	241	7.8k
Siwei Lyu United States	41	6.3k 0.8×	770 0.3×	2.0k 1.0×	630 0.9×	170 0.5×	193	7.5k
Florent Perronnin France	31	7.6k 0.9×	1.1k 0.5×	2.8k 1.4×	423 0.6×	760 2.0×	74	9.1k
Cees G. M. Snoek Netherlands	43	7.1k 0.9×	529 0.2×	2.5k 1.3×	716 1.1×	348 0.9×	209	8.3k
Yong Rui China	55	11.1k 1.4×	1.0k 0.4×	3.4k 1.7×	1.8k 2.6×	417 1.1×	203	13.3k
Alex C. Kot Singapore	47	8.1k 1.0×	1.1k 0.5×	2.8k 1.4×	1.8k 2.7×	335 0.9×	348	10.5k

Countries citing papers authored by David Doermann

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of David Doermann

This figure shows the co-authorship network connecting the top 25 collaborators of David Doermann. A scholar is included among the top collaborators of David Doermann based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Doermann. David Doermann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Zhang, Baochang, et al.. (2024). Neural Networks with Model Compression. 1 indexed citations

Chanda, Sukalpa, et al.. (2024). Scene text recognition: an Indic perspective. International Journal on Document Analysis and Recognition (IJDAR).

Sauvola, J., Sasu Tarkoma, Mika Klemettinen, Jukka Riekki, & David Doermann. (2024). Future of software development with generative AI. Automated Software Engineering. 31(1). 35 indexed citations breakdown →

Gong, Xuan, Yuxiang Bao, Yawen Huang, et al.. (2024). Federated Learning via Input-Output Collaborative Distillation. Proceedings of the AAAI Conference on Artificial Intelligence. 38(20). 22058–22066. 1 indexed citations

Alaei, Alireza, Vinh Bui, David Doermann, & Umapada Pal. (2023). Document Image Quality Assessment: A Survey. ACM Computing Surveys. 56(2). 1–36. 3 indexed citations

Yang, Yuguang, et al.. (2023). Long term 5G network traffic forecasting via modeling non-stationarity with deep learning. Communications Engineering. 2(1). 6 indexed citations

Huang, Mingzhen, et al.. (2023). Language-guided Human Motion Synthesis with Atomic Actions. 5262–5271. 5 indexed citations

Song, Liangchen, et al.. (2023). Exploring the Knowledge Transferred by Response-Based Teacher-Student Distillation. 2704–2713. 2 indexed citations

Gong, Xuan, Liangchen Song, Abhishek Sharma, et al.. (2022). Federated Learning With Privacy-Preserving Ensemble Attention Distillation. IEEE Transactions on Medical Imaging. 42(7). 2057–2067. 26 indexed citations

10.

Hannu, Jari, Christian Schuss, Mikko Rajanen, et al.. (2022). Smart mask – Wearable IoT solution for improved protection and personal health. Internet of Things. 18. 100511–100511. 36 indexed citations

11.

Xu, Sheng, Chang Liu, Baochang Zhang, et al.. (2022). BiRe-ID: Binary Neural Network for Efficient Person Re-ID. ACM Transactions on Multimedia Computing Communications and Applications. 18(1s). 1–22. 7 indexed citations

12.

Zhang, Baochang, et al.. (2021). iffDetector: Inference-Aware Feature Filtering for Object Detection. IEEE Transactions on Neural Networks and Learning Systems. 33(11). 6494–6503. 8 indexed citations

13.

Gong, Xuan, Abhishek Sharma, Srikrishna Karanam, et al.. (2021). Ensemble Attention Distillation for Privacy-Preserving Federated Learning. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 15056–15066. 89 indexed citations

14.

Ye, Peng & David Doermann. (2012). Learning features for predicting OCR accuracy. 24 indexed citations

15.

Garain, Utpal, et al.. (2012). Leveraging Statistical Transliteration for Dictionary-Based English-Bengali CLIR of OCR'd Text. International Conference on Computational Linguistics. 339–348. 5 indexed citations

16.

McNamee, Paul, James Mayfield, Veselin Stoyanov, et al.. (2011). Cross-Language Entity Linking in Maryland during a Hurricane.. Theory and applications of categories. 12 indexed citations

17.

Doermann, David, et al.. (2006). Document Image Retrieval Based on Layout Structural Similarity.. IPCV. 606–612. 21 indexed citations

18.

Doermann, David, et al.. (2003). Measuring Structural Similarity of Document Pages for Searching Document Image Databases.. 320–325. 1 indexed citations

19.

Wolf, Christian, David Doermann, & Mika Rautiainen. (2002). Video Indexing and Retrieval at UMD.. Text REtrieval Conference. 5 indexed citations

20.

Darwish, Kareem, et al.. (2001). TREC-10 Experiments at University of Maryland CLIR and Video.. Text REtrieval Conference. 549–561. 10 indexed 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.

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