Nothing Special   »   [go: up one dir, main page]

Skip to main content

The Bullinger Dataset: A Writer Adaptation Challenge

  • Conference paper
  • First Online:
Document Analysis and Recognition - ICDAR 2023 (ICDAR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14187))

Included in the following conference series:

Abstract

One of the main challenges of automatically transcribing large collections of handwritten letters is to cope with the high variability of writing styles present in the collection. In particular, the writing styles of non-frequent writers, who have contributed only few letters, are often missing in the annotated learning samples used for training handwriting recognition systems. In this paper, we introduce the Bullinger dataset for writer adaptation, which is based on the Heinrich Bullinger letter collection from the 16th century, using a subset of 3,622 annotated letters (about 1.2 million words) from 306 writers. We provide baseline results for handwriting recognition with modern recognizers, before and after the application of standard techniques for supervised adaptation of frequent writers and self-supervised adaptation of non-frequent writers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bullinger Dataset for Writer Adaptation. https://tc11.cvc.uab.es/datasets/BullingerDB_1. Accessed 30 Apr 2023

  2. Bullinger Digital project. https://www.bullinger-digital.ch. Accessed 30 Apr 2023

  3. Fink, G.A., Plötz, T.: Unsupervised estimation of writing style models for improved unconstrained off-line handwriting recognition. In: 10th International Workshop on Frontiers in Handwriting Recognition (IWFHR), pp. 1–6 (2006)

    Google Scholar 

  4. Fischer, A., Liwicki, M., Ingold, R. (eds.): Handwritten Historical Document Analysis, Recognition, and Retrieval - State of the Art and Future Trends. World Scientific (2020)

    Google Scholar 

  5. Fischer, A., Keller, A., Frinken, V., Bunke, H.: Lexicon-free handwritten word spotting using character HMMs. Pattern Recogn. Lett. 33(7), 934–942 (2012)

    Article  Google Scholar 

  6. Frinken, V., Bunke, H.: Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition. In: Proceedings of 10th International Conference on Document Analysis and Recognition (ICDAR), pp. 31–35 (2009)

    Google Scholar 

  7. Frinken, V., Fischer, A., Bunke, H., Manmatha, R.: Adapting BLSTM neural network based keyword spotting trained on modern data to historical documents. In: Proceedings of 12th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 352–357 (2010)

    Google Scholar 

  8. Granet, A., Morin, E., Mouchère, H., Quiniou, S., Viard-Gaudin, C.: Transfer learning for handwriting recognition on historical documents. In: Proceedings of 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM), pp. 1–8 (2018)

    Google Scholar 

  9. Grosicki, E., Carre, M., Brodin, J.M., Geoffrois, E.: Rimes evaluation campaign for handwritten mail processing. In: 11th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 1–6 (2008)

    Google Scholar 

  10. Jaramillo, J.C.A., Murillo-Fuentes, J.J., Olmos, P.M.: Boosting handwriting text recognition in small databases with transfer learning. In: Proceedings 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 429–434 (2018)

    Google Scholar 

  11. Kang, L., Rusinol, M., Fornes, A., Riba, P., Villegas, M.: Unsupervised writer adaptation for synthetic-to-real handwritten word recognition. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3502–3511 (2020)

    Google Scholar 

  12. Kleber, F., Fiel, S., Diem, M., Sablatnig, R.: CVL-database: an off-line database for writer retrieval, writer identification and word spotting. In: Proceedings 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 560–564 (2013)

    Google Scholar 

  13. Lavrenko, V., Rath, T.M., Manmatha, R.: Holistic word recognition for handwritten historical documents. In: Proceedings of 1st International Workshop on Document Image Analysis for Libraries, pp. 278–287 (2004)

    Google Scholar 

  14. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations (ICLR), pp. 1–18 (2019)

    Google Scholar 

  15. Marti, U.V., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition. Int. J. Doc. Anal. Recogn. 5, 39–46 (2002)

    Article  MATH  Google Scholar 

  16. Muehlberger, G., Seaward, L., Terras, M., et al.: Transforming scholarship in the archives through handwritten text recognition: Transkribus as a case study. J. Documentation 75(5), 954–976 (2019)

    Article  Google Scholar 

  17. Nosary, A., Heutte, L., Paquet, T.: Unsupervised writer adaptation applied to handwritten text recognition. Pattern Recogn. 37(2), 385–388 (2004)

    Article  MATH  Google Scholar 

  18. Puigcerver, J.: Are multidimensional recurrent layers really necessary for handwritten text recognition? In: Proceedings of 14th International Conference on Document Analysis and Recognition (ICDAR). vol. 1, pp. 67–72 (2017)

    Google Scholar 

  19. Soullard, Y., Swaileh, W., Tranouez, P., Paquet, T., Chatelain, C.: Improving text recognition using optical and language model writer adaptation. In: Proceedings of 15th International Conference on Document Analysis and Recognition (ICDAR), pp. 1175–1180 (2019)

    Google Scholar 

  20. de Sousa Neto, A.F., Bezerra, B.L.D., Toselli, A.H., Lima, E.B.: HTR-Flor: a deep learning system for offline handwritten text recognition. In: Proceedings of 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 54–61 (2020)

    Google Scholar 

  21. Strauß, T., Leifert, G., Labahn, R., Hodel, T., Mühlberger, G.: ICFHR 2018 competition on automated text recognition on a read dataset. In: Proceedings of 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 477–482 (2018)

    Google Scholar 

  22. Yang, H.M., Zhang, X.Y., Yin, F., Sun, J., Liu, C.L.: Deep transfer mapping for unsupervised writer adaptation. In: Proceedings of 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 151–156 (2018)

    Google Scholar 

Download references

Acknowledgements

This work has been supported by the Hasler Foundation, Switzerland. We would also like to thank Anna Janka, Raphael Müller, Peter Rechsteiner, Dr. Patricia Scheurer, David Selim Schoch, Dr. Raphael Schwitter, Christian Sieber, Martin Spoto, Jonas Widmer, and Dr. Beat Wolf for their contributions to the dataset and ground truth creation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anna Scius-Bertrand .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Scius-Bertrand, A., Ströbel, P., Volk, M., Hodel, T., Fischer, A. (2023). The Bullinger Dataset: A Writer Adaptation Challenge. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14187. Springer, Cham. https://doi.org/10.1007/978-3-031-41676-7_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-41676-7_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41675-0

  • Online ISBN: 978-3-031-41676-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics