Patrick Doetsch

1.1k total citations
20 papers, 610 citations indexed

About

Patrick Doetsch is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Patrick Doetsch has authored 20 papers receiving a total of 610 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 14 papers in Computer Vision and Pattern Recognition and 3 papers in Signal Processing. Recurrent topics in Patrick Doetsch's work include Natural Language Processing Techniques (15 papers), Handwritten Text Recognition Techniques (14 papers) and Image Processing and 3D Reconstruction (7 papers). Patrick Doetsch is often cited by papers focused on Natural Language Processing Techniques (15 papers), Handwritten Text Recognition Techniques (14 papers) and Image Processing and 3D Reconstruction (7 papers). Patrick Doetsch collaborates with scholars based in Germany, France and Thailand. Patrick Doetsch's co-authors include Hermann Ney, Pavel Golik, Michał Kozielski, Paul Voigtlaender, Michał, Christian Plahl, Philippe Dreuw, Ralf Schlüter, Albert Zeyer and Simon Wiesler and has published in prestigious journals such as IEEE Journal of Selected Topics in Signal Processing, Neural Information Processing Systems and RiuNet (Politechnical University of Valencia).

In The Last Decade

Patrick Doetsch

20 papers receiving 583 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Patrick Doetsch Germany 10 391 327 77 70 42 20 610
Xiaoqing Ding China 13 496 1.3× 137 0.4× 105 1.4× 93 1.3× 42 1.0× 61 728
Zhipeng Xie China 10 296 0.8× 244 0.7× 203 2.6× 104 1.5× 23 0.5× 37 731
Federico Raue Germany 7 399 1.0× 177 0.5× 91 1.2× 52 0.7× 15 0.4× 18 633
Hichem Sahbi France 16 539 1.4× 345 1.1× 47 0.6× 142 2.0× 24 0.6× 85 802
Shu Yang China 11 258 0.7× 215 0.7× 32 0.4× 127 1.8× 29 0.7× 38 569
Djamel Bouchaffra United States 9 177 0.5× 202 0.6× 75 1.0× 53 0.8× 14 0.3× 39 435
Hong-Mo Je South Korea 6 191 0.5× 201 0.6× 38 0.5× 29 0.4× 32 0.8× 10 484
Xinwen Hou China 15 722 1.8× 189 0.6× 57 0.7× 224 3.2× 35 0.8× 43 914
Anurag Arnab United States 12 669 1.7× 364 1.1× 31 0.4× 46 0.7× 28 0.7× 28 880
Xiaojie Jin China 15 508 1.3× 231 0.7× 26 0.3× 47 0.7× 18 0.4× 31 723

Countries citing papers authored by Patrick Doetsch

Since Specialization
Citations

This map shows the geographic impact of Patrick Doetsch'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 Patrick Doetsch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patrick Doetsch more than expected).

Fields of papers citing papers by Patrick Doetsch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Patrick Doetsch. 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 Patrick Doetsch. The network helps show where Patrick Doetsch may publish in the future.

Co-authorship network of co-authors of Patrick Doetsch

This figure shows the co-authorship network connecting the top 25 collaborators of Patrick Doetsch. A scholar is included among the top collaborators of Patrick Doetsch 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 Patrick Doetsch. Patrick Doetsch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zeyer, Albert, et al.. (2018). Sequence Modeling and Alignment for LVCSR-Systems. RWTH Publications (RWTH Aachen). 1–5. 5 indexed citations
2.
Golik, Pavel, Adrià Giménez, Patrick Doetsch, et al.. (2018). MLLP-UPV and RWTH Aachen Spanish ASR Systems for the IberSpeech-RTVE 2018 Speech-to-Text Transcription Challenge. 257–261. 5 indexed citations
3.
Doetsch, Patrick, Mirko Hannemann, Ralf Schlüter, & Hermann Ney. (2017). Inverted Alignments for End-to-End Automatic Speech Recognition. IEEE Journal of Selected Topics in Signal Processing. 11(8). 1265–1273. 9 indexed citations
4.
Doetsch, Patrick, Hermann Ney, Stefan Hegselmann, & Ralf Schlüter. (2016). Inverted HMM - a Proof of Concept. Neural Information Processing Systems. 3 indexed citations
5.
Doetsch, Patrick, et al.. (2016). On the Benefits of Convolutional Neural Network Combinations in Offline Handwriting Recognition. 193–198. 31 indexed citations
6.
Doetsch, Patrick, Albert Zeyer, & Hermann Ney. (2016). Bidirectional Decoder Networks for Attention-Based End-to-End Offline Handwriting Recognition. 361–366. 10 indexed citations
7.
Voigtlaender, Paul, Patrick Doetsch, & Hermann Ney. (2016). Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks. 228–233. 115 indexed citations
8.
Doetsch, Patrick, et al.. (2015). Investigation of Segmental Conditional Random Fields for large vocabulary handwriting recognition. 261–265. 1 indexed citations
9.
Voigtlaender, Paul, Patrick Doetsch, Simon Wiesler, Ralf Schlüter, & Hermann Ney. (2015). Sequence-discriminative training of recurrent neural networks. 2100–2104. 20 indexed citations
10.
Kozielski, Michał, et al.. (2014). Multilingual Off-Line Handwriting Recognition in Real-World Images. 26. 121–125. 8 indexed citations
11.
Kozielski, Michał, et al.. (2014). Towards Unsupervised Learning for Handwriting Recognition. 1. 549–554. 5 indexed citations
12.
Doetsch, Patrick, et al.. (2014). Improvement of Context Dependent Modeling for Arabic Handwriting Recognition. 2. 494–499. 6 indexed citations
13.
Doetsch, Patrick, Michał Kozielski, & Hermann Ney. (2014). Fast and Robust Training of Recurrent Neural Networks for Offline Handwriting Recognition. 279–284. 101 indexed citations
14.
Kozielski, Michał, et al.. (2014). Open-Lexicon Language Modeling Combining Word and Character Levels. 343–348. 6 indexed citations
15.
Doetsch, Patrick, et al.. (2014). The RWTH Large Vocabulary Arabic Handwriting Recognition System. 111–115. 22 indexed citations
16.
Golik, Pavel, Patrick Doetsch, & Hermann Ney. (2013). Cross-entropy vs. squared error training: a theoretical and experimental comparison. 1756–1760. 153 indexed citations
17.
Michał, et al.. (2013). Improvements in RWTH's System for Off-Line Handwriting Recognition. 935–939. 42 indexed citations
18.
Doetsch, Patrick, et al.. (2012). Comparison of Bernoulli and Gaussian HMMs Using a Vertical Repositioning Technique for Off-Line Handwriting Recognition. RiuNet (Politechnical University of Valencia). 3–7. 8 indexed citations
19.
Dreuw, Philippe, Patrick Doetsch, Christian Plahl, & Hermann Ney. (2011). Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition. 3541–3544. 36 indexed citations
20.
Doetsch, Patrick, et al.. (2009). Logistic Model Trees with AUC split criterion for the KDD cup 2009 small challenge. RWTH Publications (RWTH Aachen). 77–88. 24 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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