Skip to main content
SpringerLink
Log in
Book cover

Advances in Neural Networks: Computational and Theoretical Issues pp 347–355Cite as

Extracting Style and Emotion from Handwriting

  • Chapter

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 37))

Abstract

Humans produce handwriting samples in their every-day life activity. Handwriting can be collected on-line, thanks to digitizing tablets, or off-line by scanning paper sheets. One question is whether such handwriting data can tell about individual style, health or emotional state of the writer. We will try to answer this question by presenting related works conducted on on-line and off-line data. We will show that pieces of off-line handwriting may be useful for recognizing a writer or a handwriting style. We will also show that anxiety can be recognized as an emotion from on-line handwriting and drawings using a non parametric approach such as random forests.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Guyon, I., Schomaker, L., Plamondon, R., Liberman, R., Janet, S.: Unipen project of online data exchange and recognizer benchmarks. In: International Conference on Pattern Recognition ICPR, pp. 29–33 (1994)

    Google Scholar 

  2. Marti, U., Bunke, H.: A full english sentence database for off-line handwriting recognition. In: Proc. Int. Conf. on Doc. Analysis and Recognition, pp. 705–708 (1999)

    Google Scholar 

  3. Viard-Gaudin, C., Lallican, P.M., Binter, P., Knerr, S.: The ireste on/off (ironoff) dual handwriting database. In: ICDAR, pp. 455–458 (1999)

    Google Scholar 

  4. Bianne-Bernard, A.L., Menasri, F., El-Hajj, R., Mokbel, C., Kermorvant, C., Likforman-Sulem, L.: Dynamic and contextual information in HMM modeling for handwritten word recognition. IEEE PAMI 99(10), 2066–2080 (2011)

    Article  Google Scholar 

  5. Morillot, O., Likforman-Sulem, L., Grosicki, E.: New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks. Journal of Electronic Imaging 22(2), 23028–23028 (2013)

    Article  Google Scholar 

  6. Toselli, A.H.: Reconocimiento de Texto Manuscrito Continuo. PhD thesis, Departamento de Sistemas Informaticos y Computacion, Universidad Politecnica de Valencia (2004)

    Google Scholar 

  7. Vasilopoulos, N., Kavallieratou, E.: A classification-free word-spotting system. In: DRR (2013)

    Google Scholar 

  8. Leydier, Y., Lebourgeois, F., Emptoz, H.: Text search for medieval manuscript images. Pattern Recognition 40(12), 3552–3567 (2007)

    Article  MATH  Google Scholar 

  9. Faúndez-Zanuy, M., Hussain, A., Mekyska, J., Sesa-Nogueras, E., Monte-Moreno, E., Esposito, A., Chetouani, M., Garre-Olmo, J., Abel, A., Smékal, Z., López-de-Ipiña, K.: Biometric applications related to human beings: There is life beyond security. Cognitive Computation 5(1), 136–151 (2013)

    Article  Google Scholar 

  10. Brink, A., Smit, J., Bulacu, M., Schomaker, L.R.B.: Writer identification using directional ink-trace width measurements. Pattern Recognition 45, 162–171 (2012)

    Article  Google Scholar 

  11. Marinai, S., Fujisawa, H. (eds.): Machine Learning in Document Analysis and Recognition. SCI, vol. 90. Springer, Heidelberg (2008)

    Google Scholar 

  12. Galibert, O., Kahn, J., Oparin, I.: The first MAURDOR campaign. In: NIST OpenHart Workshop (2013)

    Google Scholar 

  13. Rosenblum, S., Parush, S., Weiss, P.: The in air phenomenon:temporal and spatial correlates of the handwriting process. Perceptual and Motor Skills, 933–954 (2003)

    Google Scholar 

  14. Kim, H.: The ClockMe system: computer-assisted screening tool for dementia. PhD thesis, Georgia Institute of Technology (2013)

    Google Scholar 

  15. Heinik, J., Werner, P., Dekel, T., Gurevitz, I., Rosenblum, S.: Computerized kinematic analysis of the clock drawing task in elderly people with mild major depressive disorder: an exploratory study. Int. Psychogeriatr, 479–488 (2010)

    Google Scholar 

  16. Plamondon, R., O’Reilly, C., Ouellet-Plamondon, C.: Strokes against stroke - strokes for strides. Pattern Recognition 47(3), 929–944 (2014)

    Article  Google Scholar 

  17. O’Reilly, C., Plamondon, R.: Design of a neuromuscular disorders diagnostic system using human movement analysis. In: ISSPA, pp. 787–792 (2012)

    Google Scholar 

  18. Longstaff, M.G., Heath, R.A.: Spiral drawing performance as an indicator of fine motor function in people with multiple sclerosis. Human Movement Science 25, 474–491 (2006)

    Article  Google Scholar 

  19. Caligiuri, M., Teulings, H., Filoteo, V., Song, D., Lohr, J.B.: Quantitative measurement of handwriting in the assessment of drug-induced parkinsonism (2006)

    Google Scholar 

  20. Sirat, C.: Ecriture et Civilisations. Editions du CNRS-IRHT (1976)

    Google Scholar 

  21. Marcelli, A., Parziale, A., Santoro, A.: Modeling handwriting style: A preliminary investigation. In: ICFHR, pp. 411–416 (2012)

    Google Scholar 

  22. Sriharia, S.N., Singerb, K.: Role of automation in the examination of handwritten items. Pattern Recognition 47, 1083–1095 (2014)

    Article  Google Scholar 

  23. Bulacu, M., Schomaker, L.: Text-independent writer identification and verification using textural and allographic features. IEEE Trans. Pattern Anal. Mach. Intell. 29, 701–717 (2007)

    Article  Google Scholar 

  24. Joutel, G., Eglin, V., Bres, S., Emptoz, H.: Curvelets based feature extraction of handwritten shapes for ancient manuscripts classification. In: DRR (2007)

    Google Scholar 

  25. Vincent, N., Bouletreau, V., Emptoz, H., Sabourin, R.: How to use fractal dimensions to qualify writings and writers. Fractals 8, 85–97 (2000)

    Google Scholar 

  26. Atanasiu, V., Likforman-Sulem, L., Vincent, N.: Writer retrieval - exploration of a novel biometric scenario using perceptual features derived from script orientation. In: ICDAR, pp. 628–632 (2011)

    Google Scholar 

  27. Eysenck, M.W., Derakshan, N., Santos, R., Calvo, M.G.: Anxiety and cognitive performance: attentional control theory. Emotion 7, 336 (2007)

    Article  Google Scholar 

  28. Izard, C.: Basic emotions, relations among emotions, and emotions-cognition relations. Psychological Review 99, 561–565 (1992)

    Article  Google Scholar 

  29. Crawford, J.R., Henry, J.D.: The depression anxiety stress scales (DASS): Normative data and latent structure in a large non-clinical sample. British Journal of Clinical Psychology 42, 111–131 (2003)

    Article  Google Scholar 

  30. Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)

    Article  MATH  Google Scholar 

  31. Team, R.C.: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2013)

    Google Scholar 

  32. Borghese, N.A., Lanzi, P.L., Mainetti, R., Pirovano, M., Surer, E.: Algorithms based on computational intelligence for autonomous physical rehabilitation at home. In: Bassis (ed.) Recent Advances of Neural Networks Models and Applications. SIST series. Springer (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut Mines-Telecom/Télécom ParisTech & CNRS LTCI, 46 rue Barrault, 75013, Paris, France

    Laurence Likforman-Sulem & Stéphan Clémençon

  2. Second University of Naples, Caserta, Naples, Italy

    Anna Esposito

  3. International Institute for Advanced Scientific Studies-IIASS, Salerno, Italy

    Anna Esposito

  4. TecnoCampus Mataro-Maresme, Escola Universitaria Politecnica de Mataro, Barcelona, Spain

    Marcos Faundez-Zanuy

Authors
  1. Laurence Likforman-Sulem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anna Esposito
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcos Faundez-Zanuy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stéphan Clémençon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laurence Likforman-Sulem .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Dipartimento di Psicologia & Vietri sul Mare (SA), Seconda Universitá di Napoli, International Institute for Advanced Scientific Studies (IIASS), Caserta, Italy

    Anna Esposito

  3. Department of Civil, Environmental, Energy, and Material Engineering, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Likforman-Sulem, L., Esposito, A., Faundez-Zanuy, M., Clémençon, S. (2015). Extracting Style and Emotion from Handwriting. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18164-6_34

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18163-9

  • Online ISBN: 978-3-319-18164-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation