Christoph Wick

409 total citations
13 papers, 41 citations indexed

About

Christoph Wick is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Christoph Wick has authored 13 papers receiving a total of 41 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 5 papers in Signal Processing. Recurrent topics in Christoph Wick's work include Handwritten Text Recognition Techniques (8 papers), Music and Audio Processing (5 papers) and Mathematics, Computing, and Information Processing (2 papers). Christoph Wick is often cited by papers focused on Handwritten Text Recognition Techniques (8 papers), Music and Audio Processing (5 papers) and Mathematics, Computing, and Information Processing (2 papers). Christoph Wick collaborates with scholars based in Germany. Christoph Wick's co-authors include Frank Puppe, Haye Hinrichsen, Kay-Michael Würzner, Tobias Strauß, Manfred Koch and Peter Lutz and has published in prestigious journals such as Applied Sciences, Computer Music Journal and Journal of New Music Research.

In The Last Decade

Christoph Wick

11 papers receiving 38 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Christoph Wick Germany	4	28	16	16	3	3	13	41
John Thickstun United States	3	16 0.6×	16 1.0×	14 0.9×	3 1.0×		6	30
Darius Afchar Austria	2	14 0.5×	19 1.2×	17 1.1×	2 0.7×		3	38
Houssem Maghrebi France	5	16 0.6×	12 0.8×	40 2.5×			7	41
Nayan Singhal United States	3	18 0.6×	30 1.9×	22 1.4×			3	40
Pauline Luc United Kingdom	2	16 0.6×	26 1.6×	19 1.2×	1 0.3×		2	39
Quchen Fu United States	5	20 0.7×	30 1.9×	33 2.1×			8	62
Herman Leung Hong Kong	7	59 2.1×	6 0.4×	28 1.8×			11	86
Julien Law-To France	4	54 1.9×	12 0.8×	9 0.6×			10	58
R. Bhaskaran India	3	16 0.6×	7 0.4×	14 0.9×			8	40

Countries citing papers authored by Christoph Wick

Since Specialization

Citations

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

Fields of papers citing papers by Christoph Wick

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Christoph Wick

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

All Works

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13 of 13 papers shown

1.

Wick, Christoph & Frank Puppe. (2021). Experiments and detailed error-analysis of automatic square notation transcription of medieval music manuscripts using CNN/LSTM-networks and a neume dictionary. Journal of New Music Research. 50(1). 18–36. 8 indexed citations

2.

Wick, Christoph, et al.. (2021). tfaip - a Generic and Powerful Research Framework for Deep Learning based on Tensorflow. The Journal of Open Source Software. 6(62). 3297–3297. 1 indexed citations

3.

Wick, Christoph, et al.. (2020). Lyrics Recognition and Syllable Assignment of Medieval Music Manuscripts. 1 indexed citations

4.

Wick, Christoph. (2020). Optical Medieval Music Recognition. Online Publication Service of Würzburg University (Würzburg University).

5.

Wick, Christoph, et al.. (2019). Automatic Semantic Text Tagging on Historical Lexica by Combining OCR and Typography Classification. Zenodo (CERN European Organization for Nuclear Research). 33–38.

6.

Wick, Christoph, et al.. (2019). Staff, Symbol and Melody Detection of Medieval Manuscripts Written in Square Notation Using Deep Fully Convolutional Networks. Applied Sciences. 9(13). 2646–2646. 4 indexed citations

7.

Wick, Christoph, et al.. (2018). Playing Music in Just Intonation: A Dynamically Adaptive Tuning Scheme. Computer Music Journal. 42(3). 47–62. 2 indexed citations

8.

Wick, Christoph, et al.. (2018). Comparison of OCR Accuracy on Early Printed Books using the Open Source Engines Calamari and OCRopus. Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt). 33(1). 79–96. 12 indexed citations

9.

Wick, Christoph, et al.. (2018). Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning. arXiv (Cornell University). 33. 3–24. 1 indexed citations

10.

Wick, Christoph, et al.. (2018). Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning. Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt). 33(1). 3–24. 6 indexed citations

11.

Wick, Christoph. (2016). Deep Learning. Informatik-Spektrum. 40(1). 103–107. 3 indexed citations

12.

Wick, Christoph, et al.. (1996). Der ISO 9241-Evaluator. 27. 13–17. 1 indexed citations

13.

Wick, Christoph, et al.. (1993). Individual learning nurtures J.P. Morgan. Personnel journal. 2 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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