2026 David Doermann: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	74	1501	1456	782	754	361	21118

Research.com Recognitions

2016 - IAPR P. Zamperoni Award Content Selection Using Frontalness of Multiple Frames
2014 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to the field of document image analysis and in recognition of service to the IAPR
2014 - IEEE Fellow For contributions to research and development of automatic analysis and processing of document page imaging

Overview

David Doermann is affiliated with the University at Buffalo, State University of New York in the United States. Their research predominantly falls within the field of Computer Science, with a strong focus on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Aerospace Engineering, and Information Systems.

Their work covers a variety of topics related to advanced computational methods, including:

Advanced Neural Network Applications
Domain Adaptation and Few-Shot Learning
Human Pose and Action Recognition
Video Surveillance and Tracking Methods
Anomaly Detection Techniques and Applications
Advanced Image and Video Retrieval Techniques
Adversarial Robustness in Machine Learning

David Doermann has contributed extensively to scholarly publication venues, with multiple papers appearing in:

arXiv (Cornell University)
International Journal of Computer Vision
Proceedings of the AAAI Conference on Artificial Intelligence
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
IEEE Transactions on Pattern Analysis and Machine Intelligence

Frequent collaborators within their research include Baochang Zhang, Junsong Yuan, Ziyan Wu, Guodong Guo, and Xuan Gong.

Notable recent publications by David Doermann include:

YOLOv12: Attention-Centric Real-Time Object Detectors, 2025, arXiv (Cornell University)
Ensemble Attention Distillation for Privacy-Preserving Federated Learning, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Chart Mining: A Survey of Methods for Automated Chart Analysis, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
Future of software development with generative AI, 2024, Automated Software Engineering
Multi-UAV Mobile Edge Computing and Path Planning Platform Based on Reinforcement Learning, 2021, IEEE Transactions on Emerging Topics in Computational Intelligence

In addition to articles and conference papers, David Doermann has published books with Springer International Publishing. One book is titled Neural Networks with Model Compression (2024).

Throughout their career, they have received several awards, including:

IAPR P. Zamperoni Award (2016) for work on content selection using frontalness of multiple frames
Fellow of the International Association for Pattern Recognition (IAPR) (2014) for contributions to document image analysis and service to IAPR
IEEE Fellow (2014) for contributions to research and development of automatic analysis and processing of document page imaging

Best Publications

Convolutional Neural Networks for No-Reference Image Quality Assessment

Le Kang;Peng Ye;Yi Li;David Doermann

1337
Citations
Automatic text detection and tracking in digital video

Huiping Li;D. Doermann;O. Kia

934
Citations
Text Detection and Recognition in Imagery: A Survey

Qixiang Ye;David Doermann

887
Citations
Unsupervised feature learning framework for no-reference image quality assessment

Peng Ye;Jayant Kumar;Le Kang;David Doermann

846
Citations
Camera-based analysis of text and documents: a survey

Jian Liang;David Doermann;Huiping Li

674
Citations
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning

Shaohui Lin;Rongrong Ji;Chenqian Yan;Baochang Zhang

561
Citations
Blind Image Quality Assessment Based on High Order Statistics Aggregation

Jingtao Xu;Peng Ye;Qiaohong Li;Haiqing Du

483
Citations
The Indexing and Retrieval of Document Images

David Doermann

441
Citations
Video summarization by curve simplification

Daniel DeMenthon;Vikrant Kobla;David Doermann

423
Citations
Progress in camera-based document image analysis

D. Doermann;Jian Liang;Huiping Li

409
Citations
Robust point matching for nonrigid shapes by preserving local neighborhood structures

Yefeng Zheng;D. Doermann

400
Citations
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks

Xiaolong Jiang;Zehao Xiao;Baochang Zhang;Xiantong Zhen

399
Citations
No-Reference Image Quality Assessment Using Visual Codebooks

Peng Ye;D. Doermann

350
Citations
Review of Classifier Combination Methods

Sergey Tulyakov;Stefan Jaeger;Venu Govindaraju;David S. Doermann

294
Citations
Script-Independent Text Line Segmentation in Freestyle Handwritten Documents

Yi Li;Yefeng Zheng;D. Doermann;S. Jaeger

281
Citations
Tools and techniques for video performance evaluation

D. Doermann;D. Mihalcik

281
Citations
Machine printed text and handwriting identification in noisy document images

Yefeng Zheng;Huiping Li;D. Doermann

276
Citations
Hierarchical Part-Template Matching for Human Detection and Segmentation

Zhe Lin;L.S. Davis;D. Doermann;D. DeMenthon

253
Citations
Geometric Rectification of Camera-Captured Document Images

Jian Liang;D. DeMenthon;D. Doermann

224
Citations
Handbook of Document Image Processing and Recognition

David Doermann;Karl Tombre

218
Citations

Frequent Co-Authors

Daniel DeMenthon Johns Hopkins University Applied Physics Laboratory

Baochang Zhang Beihang University

Azriel Rosenfeld University of Maryland, College Park

Yefeng Zheng Tencent (China)

Larry S. Davis University of Maryland, College Park

Douglas W. Oard University of Maryland, College Park

Rongrong Ji Xiamen University

Jianzhuang Liu Shenzhen Institutes of Advanced Technology

Qixiang Ye Chinese Academy of Sciences

Ehud Rivlin Technion – Israel Institute of Technology

External Links

Wikipedia of David Doermann Personal website of David Doermann Google Scholar page

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