Wick, Christoph

Christoph Wick, M.Sc.

University of Würzburg
Department for Artificial Intelligence
and Applied Computer Science
Am Hubland
D-97074 Würzburg

Room: B015
Phone: +49 931 / 31 - 82067
christoph.wick@uni-wuerzburg.de

PGP-Key: 8E431938 (4A54 9836 DC22 EDDA ED80 992F AE8E FF7A 8E43 1938)

Projects

Deep Learning
Entropy Piano Tuner

Publications

Wick, C., and Puppe, F. (2019) OMMR4all — a Semiautomatic Online Editor for Medieval Music Notations. In 2nd International Workshop on Reading Music Systems (Calvo-Zaragoza, J., and Pacha, A., Eds.), p. 31–34, Delft, The Netherlands.

[ BibTeX ] [ URL ]

Reul, C., Springmann, U., Wick, C., and Puppe, F. (2019) State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines, DHd 2019 Digital Humanities: multimedial & multimodal.

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Reul, C., Göttel, S., Springmann, U., Wick, C., Würzner, K.-M., and Puppe, F. (2019) Automatic Semantic Text Tagging on Historical Lexica by Combining OCR and Typography Classification, Proceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage.

[ BibTeX ]

Reul, C., Christ, D., Hartelt, A., Balbach, N., Wehner, M., Springmann, U., Wick, C., Grundig, C., Büttner, A., and Puppe, F. (2019) OCR4all - An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings, ArXiv Preprints (submitted to MDPI - Applied Sciences).

[ BibTeX ] [ URL ]

@article{reul2019ocr4all,
  abstract = {Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout recognition and segmentation, character recognition and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. A comfortable GUI allows error corrections not only in the final output, but already in early stages to minimize error propagations. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. Experiments showed that users with minimal or no experience were able to capture the text of even the earliest printed books with manageable effort and great quality, achieving excellent character error rates (CERs) below 0.5%. The fully automated application on 19th century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.},
  author = {Reul, Christian and Christ, Dennis and Hartelt, Alexander and Balbach, Nico and Wehner, Maximilian and Springmann, Uwe and Wick, Christoph and Grundig, Christine and Büttner, Andreas and Puppe, Frank},
  journal = {ArXiv Preprints (submitted to MDPI - Applied Sciences)},
  keywords = {myown},
  title = {OCR4all - An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings},
  year = 2019
}

Wick, C., Hartelt, A., and Puppe, F. (2019) Staff, Symbol and Melody Detection of Medieval Manuscripts Written in Square Notation Using Deep Fully Convolutional Networks, Applied Sciences 9.

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@article{app9132646,
  abstract = {Even today, the automatic digitisation of scanned documents in general, but especially the automatic optical music recognition (OMR) of historical manuscripts, still remains an enormous challenge, since both handwritten musical symbols and text have to be identified. This paper focuses on the Medieval so-called square notation developed in the 11th&ndash;12th century, which is already composed of staff lines, staves, clefs, accidentals, and neumes that are roughly spoken connected single notes. The aim is to develop an algorithm that captures both the neumes, and in particular its melody, which can be used to reconstruct the original writing. Our pipeline is similar to the standard OMR approach and comprises a novel staff line and symbol detection algorithm based on deep Fully Convolutional Networks (FCN), which perform pixel-based predictions for either staff lines or symbols and their respective types. Then, the staff line detection combines the extracted lines to staves and yields an F 1 -score of over 99% for both detecting lines and complete staves. For the music symbol detection, we choose a novel approach that skips the step to identify neumes and instead directly predicts note components (NCs) and their respective affiliation to a neume. Furthermore, the algorithm detects clefs and accidentals. Our algorithm predicts the symbol sequence of a staff with a diplomatic symbol accuracy rate (dSAR) of about 87%, which includes symbol type and location. If only the NCs without their respective connection to a neume, all clefs and accidentals are of interest, the algorithm reaches an harmonic symbol accuracy rate (hSAR) of approximately 90%. In general, the algorithm recognises a symbol in the manuscript with an F 1 -score of over 96%.},
  author = {Wick, Christoph and Hartelt, Alexander and Puppe, Frank},
  journal = {Applied Sciences},
  keywords = {cnn dissertation dnn myown},
  number = 13,
  title = {Staff, Symbol and Melody Detection of Medieval Manuscripts Written in Square Notation Using Deep Fully Convolutional Networks},
  volume = 9,
  year = 2019
}

Reul, C., Springmann, U., Wick, C., and Puppe, F. (2018) Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning, JLCL 33, 3–24.

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Wick, C., Reul, C., and Puppe, F. (2018) Comparison of OCR Accuracy on Early Printed Books using the Open Source Engines Calamari and OCRopus, JLCL, pp. 79–96.

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Reul, C., Wick, C., Springmann, U., and Puppe, F. (2017) Transfer Learning for OCRopus Model Training on Early Printed Books.

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Wick, C., and Puppe, F. (2017) Leaf Identification Using a Deep Convolutional Neural Network.

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10.

Reul, C., Springmann, U., Wick, C., and Puppe, F. (2017) Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting.

[ BibTeX ] [ URL ]

11.

Wick, C., and Puppe, F. (2017) Fully Convolutional Neural Networks for Page Segmentation of Historical Document Images.

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12.

Stange, K., Wick, C., and Hinrichsen, H. (2017) Playing Music in Just Intonation - A Dynamically Adapting Tuning Scheme.

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13.

Wick, C. (2016) Deep Learning, Informatik-Spektrum 1–5.

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14.

Wick, C., Um, J., and Hinrichsen, H. (2015) Entanglement formation under random interactions.

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Teaching

Seminar "Aktuelle Trends der künstlichen Intelligenz" - "Deep Learning" (winter term 2015/2016)
Künstliche Intelligenz 2 (winter term 2015/2016)
Seminar "Aktuelle Trends der künstlichen Intelligenz" - Wissensmodellierung (summer term 2016)
Softwaretechnik (summer term 2016)

Wick, Christoph

Christoph Wick, M.Sc.

Projects

Publications

Teaching

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