Skip to main content
SpringerLink
Log in
SpringerLink
Log in

\(\textit{TexT}\) - Text Extractor Tool for Handwritten Document Transcription and Annotation

  • Conference paper
  • First Online:
Digital Libraries and Multimedia Archives (IRCDL 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 806))

Included in the following conference series:

Abstract

This paper presents a framework for semi-automatic transcription of large-scale historical handwritten documents and proposes a simple user-friendly text extractor tool, \(\textit{TexT}\) for transcription. The proposed approach provides a quick and easy transcription of text using computer assisted interactive technique. The algorithm finds multiple occurrences of the marked text on-the-fly using a word spotting system. \(\textit{TexT}\) is also capable of performing on-the-fly annotation of handwritten text with automatic generation of ground truth labels, and dynamic adjustment and correction of user generated bounding box annotations with the word being perfectly encapsulated. The user can view the document and the found words in the original form or with background noise removed for easier visualization of transcription results. The effectiveness of \(\textit{TexT}\) is demonstrated on an archival manuscript collection from well-known publicly available dataset.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Mori, S., Nishida, H., Yamada, H.: Optical Character Recognition. Wiley, New York (1999)

    Google Scholar 

  2. Govindan, V.K., Shivaprasad, A.P.: Character recognition - a review. Pattern Recogn. 23(7), 671–683 (1990)

    Article  Google Scholar 

  3. Blanke, T., Bryant, M., Hedges, M.: Open source optical character recognition for historical research. J. Doc. 68(5), 659–683 (2012)

    Article  Google Scholar 

  4. Plamondon, R., Srihari, S.N.: Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 63–84 (2000)

    Article  Google Scholar 

  5. Marti, U.V., Bunke, H.: Hidden Markov Models, pp. 65–90. World Scientific Publishing Co., Inc., River Edge (2002)

    Google Scholar 

  6. Toselli, A.H., Vidal, E.: Handwritten text recognition results on the Bentham collection with improved classical N-gram-HMM methods. In: Proceedings of the 3rd International Workshop on Historical Document Imaging and Processing, HIP 2015, pp. 15–22. ACM, New York (2015)

    Google Scholar 

  7. Espana-Boquera, S., Castro-Bleda, M.J., Gorbe-Moya, J., Zamora-Martinez, F.: Improving offline handwritten text recognition with hybrid HMM/ANN models. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 767–779 (2011)

    Article  Google Scholar 

  8. Parvez, M.T., Mahmoud, S.A.: Offline Arabic handwritten text recognition: a survey. ACM Comput. Sur. 45(2), 23:1–23:35 (2013)

    MATH  Google Scholar 

  9. http://urn.kb.se/resolve?urn=urn:nbn:se:alvin:portal:record-12537/ (2017)

  10. Howe, J.: The rise of crowdsourcing. Wired Mag. 14(6), 1–4 (2006)

    Google Scholar 

  11. Moyle, M., Tonra, J., Wallace, V.: Manuscript transcription by crowdsourcing: transcribe bentham. Liber Q. 20(3–4), 347–356 (2011)

    Article  Google Scholar 

  12. http://blogs.ucl.ac.uk/transcribe-bentham/2017/08/21/transcription-update-22-july-to-18-august-2017/ (2017)

  13. http://read.transkribus.eu/

  14. http://transkribus.eu/Transkribus/

  15. Borne, K., Team, Z.: The zooniverse: a framework for knowledge discovery from citizen science data. In: AGU Fall Meeting Abstracts (2011)

    Google Scholar 

  16. http://scripto.org/

  17. http://velehanden.nl/ (2017)

  18. http://transcription.si.edu/ (2017)

  19. http://siarchives.si.edu/blog/smithsonian-crowdsourcing-1849/ (2017)

  20. http://www.alvin-portal.org/ (2017)

  21. http://urn.kb.se/resolve?urn=urn:nbn:se:alvin:portal:record-101351/ (2017)

  22. Hast, A., Fornés, A.: A segmentation-free handwritten word spotting approach by relaxed feature matching. In: 2016 12th IAPR Workshop on Document Analysis Systems (DAS), pp. 150–155. IEEE (2016)

    Google Scholar 

  23. Héroux, P., Barbu, E., Adam, S., Trupin, É.: Automatic ground-truth generation for document image analysis and understanding. In: Ninth International Conference on Document Analysis and Recognition, ICDAR 2007, pp. 476–480. IEEE (2007)

    Google Scholar 

  24. Pletschacher, S., Antonacopoulos, A.: The page (page analysis and ground-truth elements) format framework. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 257–260. IEEE (2010)

    Google Scholar 

  25. Yanikoglu, B.A., Vincent, L.: Pink panther: a complete environment for ground-truthing and benchmarking document page segmentation. Pattern Recogn. 31(9), 1191–1204 (1998)

    Article  Google Scholar 

  26. Kanungo, T., Lee, C.H., Czorapinski, J., Bella, I.: TRUEVIZ: a groundtruth/metadata editing and visualizing toolkit for OCR. In: Document Recognition and Retrieval VIII, vol. 4307, pp. 1–13. International Society for Optics and Photonics (2000)

    Google Scholar 

  27. Yacoub, S., Saxena, V., Sami, S.N.: PerfectDoc: a ground truthing environment for complex documents. In: Proceedings of the Eighth International Conference on Document Analysis and Recognition, pp. 452–456. IEEE (2005)

    Google Scholar 

  28. Saund, E., Lin, J., Sarkar, P.: PixLabeler: user interface for pixel-level labeling of elements in document images. In: 10th International Conference on Document Analysis and Recognition, ICDAR 2009, pp. 646–650. IEEE (2009)

    Google Scholar 

  29. Doermann, D., Zotkina, E., Li, H.: GEDI - a groundtruthing environment for document images. In: Ninth IAPR International Workshop on Document Analysis Systems (DAS) (2010)

    Google Scholar 

  30. Clausner, C., Pletschacher, S., Antonacopoulos, A.: Aletheia - an advanced document layout and text ground-truthing system for production environments. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 48–52. IEEE (2011)

    Google Scholar 

  31. Biller, O., Asi, A., Kedem, K., El-Sana, J., Dinstein, I.: WebGT: an interactive web-based system for historical document ground truth generation. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 305–308. IEEE (2013)

    Google Scholar 

  32. Valsecchi, F., Abrate, M., Bacciu, C., Piccini, S., Marchetti, A.: Text encoder and annotator: an all-in-one editor for transcribing and annotating manuscripts with RDF. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 399–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47602-5_52

    Chapter  Google Scholar 

  33. Antonacopoulos, A., Karatzas, D., Bridson, D.: Ground truth for layout analysis performance evaluation. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 302–311. Springer, Heidelberg (2006). https://doi.org/10.1007/11669487_27

    Chapter  Google Scholar 

  34. Vats, E., Hast, A.: On-the-fly historical handwritten text annotation. In: Proceedings of the 2017 Workshop on Human-Document Interaction (2017, in press)

    Google Scholar 

  35. Wei, H., Seuret, M., Liwicki, M., Ingold, R.: The use of Gabor features for semi-automatically generated polyon-based ground truth of historical document images. Digit. Scholarsh. Humanit. 32(1), i134–i149 (2017)

    Article  Google Scholar 

  36. Romero, V., Bosch, V., Hernández, C., Vidal, E., Sánchez, J.A.: A historical document handwriting transcription end-to-end system. In: Alexandre, L.A., Salvador Sánchez, J., Rodrigues, J.M.F. (eds.) IbPRIA 2017. LNCS, vol. 10255, pp. 149–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58838-4_17

    Chapter  Google Scholar 

  37. Terrades, O.R., Toselli, A.H., Serrano, N., Romero, V., Vidal, E., Juan, A.: Interactive layout analysis and transcription systems for historic handwritten documents. In: 10th ACM Symposium on Document Engineering, pp. 219–222 (2010)

    Google Scholar 

  38. Serrano, N., Pérez, D., Sanchis, A., Juan, A.: Adaptation from partially supervised handwritten text transcriptions. In: Proceedings of the 2009 International Conference on Multimodal Interfaces, ICMI-MLMI 2009, pp. 289–292. ACM, New York (2009)

    Google Scholar 

  39. Serrano, N., Giménez, A., Sanchis, A., Juan, A.: Active learning strategies for handwritten text transcription. In: International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, ICMI-MLMI 2010, pp. 48:1–48:4. ACM, New York (2010)

    Google Scholar 

  40. Romero, V., Toselli, A.H., Vidal, E.: Multimodal Interactive Handwritten Text Transcription, vol. 80. World Scientific, Singapore (2012)

    MATH  Google Scholar 

  41. https://www.prhlt.upv.es/wp/project/2016/idoc

  42. Bosch, V., Toselli, A.H., Vidal, E.: Semiautomatic text baseline detection in large historical handwritten documents. In: 2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 690–695. IEEE (2014)

    Google Scholar 

  43. Giotis, A.P., Sfikas, G., Gatos, B., Nikou, C.: A survey of document image word spotting techniques. Pattern Recogn. 68, 310–332 (2017)

    Article  Google Scholar 

  44. Vats, E., Hast, A., Singh, P.: Automatic document image binarization using Bayesian optimization. In: Proceedings of the 2017 Workshop on Historical Document Imaging and Processing. ACM (2017, in press)

    Google Scholar 

  45. Kittur, A., Nickerson, J.V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., Lease, M., Horton, J.: The future of crowd work. In: Proceedings of the 2013 conference on Computer Supported Cooperative Work, pp. 1301–1318. ACM (2013)

    Google Scholar 

  46. Romero, V., Fornés, A., Serrano, N., Sánchez, J.A., Toselli, A.H., Frinken, V., Vidal, E., Lladós, J.: The ESPOSALLES database: an ancient marriage license corpus for off-line handwriting recognition. Pattern Recogn. 46(6), 1658–1669 (2013)

    Article  Google Scholar 

  47. Fernández-Mota, D., Almazán, J., Cirera, N., Fornés, A., Lladós, J.: BH2M: the Barcelona historical, handwritten marriages database. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 256–261. IEEE (2014)

    Google Scholar 

Download references

Acknowledgment

This work was supported by the Riksbankens Jubileumsfond (Dnr NHS14-2068:1) and the Swedish strategic research programme eSSENCE.

Author information

Authors and Affiliations

  1. Department of Information Technology, Uppsala University, Uppsala, Sweden

    Anders Hast & Ekta Vats

  2. University Library, Uppsala University, Uppsala, Sweden

    Per Cullhed

Authors
  1. Anders Hast
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Per Cullhed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ekta Vats
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ekta Vats .

Editor information

Editors and Affiliations

  1. University of Udine, Udine, Italy

    Giuseppe Serra

  2. University of Udine, Udine, Italy

    Carlo Tasso

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hast, A., Cullhed, P., Vats, E. (2018). \(\textit{TexT}\) - Text Extractor Tool for Handwritten Document Transcription and Annotation. In: Serra, G., Tasso, C. (eds) Digital Libraries and Multimedia Archives. IRCDL 2018. Communications in Computer and Information Science, vol 806. Springer, Cham. https://doi.org/10.1007/978-3-319-73165-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-73165-0_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73164-3

  • Online ISBN: 978-3-319-73165-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation