Skip to main content
SpringerLink
Log in

Interactive handwriting recognition with limited user effort

  • Original Paper
  • Published:
International Journal on Document Analysis and Recognition (IJDAR) Aims and scope Submit manuscript

Abstract

Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. Although post-editing automatic recognition of handwritten text is feasible, it is not clearly better than simply ignoring it and transcribing the document from scratch. A more effective approach is to follow an interactive approach in which both the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Nevertheless, in some applications, the user effort available to transcribe documents is limited and fully supervision of the system output is not realistic. To circumvent these problems, we propose a novel interactive approach which efficiently employs user effort to transcribe a document by improving three different aspects. Firstly, the system employs a limited amount of effort to solely supervise recognised words that are likely to be incorrect. Thus, user effort is efficiently focused on the supervision of words for which the system is not confident enough. Secondly, it refines the initial transcription provided to the user by recomputing it constrained to user supervisions. In this way, incorrect words in unsupervised parts can be automatically amended without user supervision. Finally, it improves the underlying system models by retraining the system from partially supervised transcriptions. In order to prove these statements, empirical results are presented on two real databases showing that the proposed approach can notably reduce user effort in the transcription of handwritten text in (old) documents.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Agua, M., Serrano, N., Civera, J., Juan, A.: Character-based handwritten text recognition of multilingual documents. In: Proceedings of Advances in Speech and Language Technologies for Iberian Languages (IBERSPEECH 2012), Madrid (Spain), pp. 187–196 (2012)

  2. Ahn, L.V., Maurer, B., Mcmillen, C., Abraham, D., Blum, M.: reCAPTCHA: human-based character recognition via web security measures. Science 321, 1465–1468 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  3. Barrachina, S., Bender, O., Casacuberta, F., Civera, J., Cubel, E., Khadivi, S., Lagarda, A.L., Ney, H., Tomás, J., Vidal, E.: Statistical approaches to computer-assisted translation. Comput. Linguist. 35(1), 3–28 (2009)

    Article  MathSciNet  Google Scholar 

  4. Bertolami, R., Bunke, H.: Hidden markov model-based ensemble methods for offline handwritten text line recognition. Pattern Recognit. 41, 3452–3460 (2008)

    Article  MATH  Google Scholar 

  5. Bunke, H., Bengio, S., Vinciarelli, A.: Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 709–720 (2004)

    Article  Google Scholar 

  6. Dreuw, P., Jonas, S., Ney, H.: White-space models for offline Arabic handwriting recognition. In: Proceedings of the 19th International Conference on, Pattern Recognition, pp. 1–4 (2008)

  7. Efron, B., Tibshirani, R.J.: An introduction to bootstrap. Chapman and Hall/CRC, London (1994)

    Google Scholar 

  8. Fischer, A., Wuthrich, M., Liwicki, M., Frinken, V., Bunke, H., Viehhauser, G., Stolz, M.: Automatic transcription of handwritten medieval documents. In: Proceedings of the 15th International Conference on Virtual Systems and Multimedia, pp. 137–142 (2009)

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

  10. Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)

    Article  Google Scholar 

  11. Hakkani-Tür, D., Riccardi, G., Tur, G.: An active approach to spoken language processing. ACM Trans. Speech Lang. Process. 3, 1–31 (2006)

    Article  Google Scholar 

  12. Kristjannson, T., Culotta, A., Viola, P., McCallum, A.: Interactive information extraction with constrained conditional random fields. In: Proceedings of the 19th Natural Conference on Artificial Intelligence, San Jose, CA (USA), pp. 412–418 (2004)

  13. Laurence Likforman-Sulem, A.Z., Taconet, B.: Text line segmentation of historical documents: a survey. Int. J. Doc. Anal. Recognit. 9, 123–138 (2007)

    Article  Google Scholar 

  14. Le Bourgeois, F., Emptoz, H.: Debora: digital access to books of the renaissance. Int. J. Doc. Anal. Recognit. 9, 193–221 (2007)

    Article  Google Scholar 

  15. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)

    MathSciNet  Google Scholar 

  16. Neal, R.M., Hinton, G.E.: Learning in graphical models. In: A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants, Chap. MIT Press, Cambridge, MA, USA, pp. 355–368 (1999)

  17. Pérez, D., Tarazón, L., Serrano, N., Ramos-Terrades, O., Juan, A.: The GERMANA database. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, Barcelona (Spain), pp. 301–305 (2009)

  18. Plötz, T., Fink, G.A.: Markov models for offline handwriting recognition: a survey. Int. J. Doc. Anal. Recognit. 12(4), 269–298 (2009)

    Article  Google Scholar 

  19. Quiniou, S., Cheriet, M., Anquetil, E.: Error handling approach using characterization and correction steps for handwritten document analysis. Int. J. Doc. Anal. Recognit. 15(2), 125–141 (2012)

    Article  Google Scholar 

  20. Rodríguez, L., García-Varea, I., Vidal, E.: Multi-modal computer assisted speech transcription. In: International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, ACM, New York, NY, USA, pp. 30:1–30:7 (2010)

  21. Serrano, N., Pérez, D., Sanchis, A., Juan, A.: Adaptation from partially supervised handwritten text transcriptions. In: Proceedings of the 11th International Conference on Multimodal Interfaces and the 6th Workshop on Machine Learning for Multimodal Interaction, Cambridge, MA (USA), pp. 289–292 (2009)

  22. Serrano, N., Castro, F., Juan, A.: The RODRIGO database. In: Proceedings of the 7th International Conference on Language Resources and Evaluation, Valleta (Malta), pp. 2709–2712 (2010)

  23. Serrano, N., Giménez, A., Sanchis, A., Juan, A.: Active learning strategies for handwritten text transcription. In: Proceedings of the 12th International Conference on Multimodal Interfaces and the 7th Workshop on Machine Learning for Multimodal, Interaction, Beijing (China) (2010)

  24. Serrano, N., Sanchis, A., Juan, A.: Balancing error and supervision effort in interactive-predictive handwriting recognition. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, Hong Kong (China), pp. 373–376 (2010)

  25. Serrano, N., Tarazón, L., Pérez, D., Ramos-Terrades, O., Juan, A.: The GIDOC prototype. In: Proceedings of the 10th International Workshop on Pattern Recognition in Information Systems, Funchal (Portugal), pp. 82–89 (2010)

  26. Settles, B.: Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison (2009)

  27. Tarazón, L., Pérez, D., Serrano, N., Alabau, V., Ramos-Terrades, O., Sanchis, A., Juan, A.: Confidence measures for error correction in interactive transcription of handwritten text. In: Proceedings of the 15th International Conference on Image Analysis, Processing, Vietri sul Mare (Italy) (2009)

  28. Toselli, A., Juan, A., Keysers, D., González, J., Salvador, I., Ney, H., Vidal, E., Casacuberta, F.: Integrated handwriting recognition and interpretation using finite-state models. Int. J. Pattern Recognit. Artif. Intell. 18(4), 519–539 (2004)

    Google Scholar 

  29. Toselli, A., Romero, V., Rodríguez, L., Vidal, E.: Computer assisted transcription of handwritten text. In: Proceedings of the 9th International Conference on Document Analysis and Recognition, Curitiba (Brazil), pp. 944–948 (2007)

  30. Valor, J., Pérez, A., Civera, J., Juan, A.: Integrating a state-of-the-art ASR system into the opencast Matterhorn platform. In: Proceedings of the Advances in Speech and Language Technologies for Iberian Languages (IBERSPEECH 2012), Madrid (Spain), pp. 237–246 (2012)

  31. Wessel, F., Ney, H.: Unsupervised training of acoustic models for large vocabulary continuous speech recognition. IEEE Trans Speech Audio Process 13(1), 23–31 (2005)

    Article  Google Scholar 

Download references

Acknowledgments

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No 287755 (transLectures). Also supported by the Spanish Government (MICINN, MITyC, ”Plan E”, under Grants MIPRCV ”Consolider Ingenio 2010”, MITTRAL (TIN2009-14633-C03-01), erudito.com (TSI-020110-2009-439), iTrans2 (TIN2009-14511), and FPU (AP2007-02867), and the Generalitat Valenciana (Grants Prometeo/2009/014 and GV/2010/067).

Author information

Authors and Affiliations

  1. DSIC, Universitat Politècnica de València, Camí de Vera s/n, 46022 , Valencia, Spain

    Nicolás Serrano, Adrià Giménez, Jorge Civera, Alberto Sanchis & Alfons Juan

Authors
  1. Nicolás Serrano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adrià Giménez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jorge Civera
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alberto Sanchis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alfons Juan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicolás Serrano.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Serrano, N., Giménez, A., Civera, J. et al. Interactive handwriting recognition with limited user effort. IJDAR 17, 47–59 (2014). https://doi.org/10.1007/s10032-013-0204-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10032-013-0204-5

Keywords

Search

Navigation