Advertisement

An Answer to “Who Needs a Stylus?” on Handwriting Recognition on Mobile Devices

  • Andreas Holzinger
  • Gig Searle
  • Bernhard Peischl
  • Matjaz Debevc
Part of the Communications in Computer and Information Science book series (CCIS, volume 314)

Abstract

”Who needs a stylus?” asked the late Steve Jobs during his introduction of the iPhone. Interestingly, just at this time, Apple had made a patent application in handwriting and input recognition via pen, and Google and Nokia followed. So, “who needs a stylus then?” According to our experience in projects with mobile devices in the “real-world” we noticed that handwriting is still an issue, e.g. in the medical domain. Medical professionals are very accustomed to use a pen, whereas touch devices are rather used by non-medical professionals and definitely preferred by elderly people. During our projects on mobile devices, we noticed that both handwriting and touch has certain advantages and disadvantages, but that both are of equal importance. So to concretely answer “Who needs a stylus?” we can answer: Medical professionals for example. And this is definitely a large group of users.

Keywords

Handwriting recognition Pen-based input Mobile computer Human-computer interaction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yaeger, L.S., Fabrick, R.W., Pagallo, G.M.: Method and Apparatus for Acquiring and Organizing Ink Information in Pen-Aware Computer Systems 20090279783, Patent issuedGoogle Scholar
  2. 2.
    Yaeger, L.S., Webb, B.J., Lyon, R.F.: Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in the Newton. In: Orr, G.B., Müller, K.-R. (eds.) NIPS-WS 1996. LNCS, vol. 1524, pp. 275–298. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Holzinger, A.: User-Centered Interface Design for Disabled and Elderly People: First Experiences with Designing a Patient Communication System (PACOSY). In: Proceedings of the 8th International Conference on Computers Helping People with Special Needs, pp. 33–40 (2002)Google Scholar
  4. 4.
    Holzinger, A.: Finger Instead of Mouse: Touch Screens as a Means of Enhancing Universal Access. In: Carbonell, N., Stephanidis, C. (eds.) UI4ALL 2002. LNCS, vol. 2615, pp. 387–397. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Holzinger, A., Kosec, P., Schwantzer, G., Debevc, M., Hofmann-Wellenhof, R., Frühauf, J.: Design and Development of a Mobile Computer Application to Reengineer Workflows in the Hospital and the Methodology to evaluate its Effectiveness. Journal of Biomedical Informatics 44(6), 968–977 (2011)CrossRefGoogle Scholar
  6. 6.
    Holzinger, A., Höller, M., Schedlbauer, M., Urlesberger, B.: An Investigation of Finger versus Stylus Input in Medical Scenarios. In: Luzar-Stiffler, V., Dobric, V.H., Bekic, Z. (eds.) ITI 2008: 30th International Conference on Information Technology Interfaces, pp. 433–438. IEEE (2008)Google Scholar
  7. 7.
    Holzinger, A., Baernthaler, M., Pammer, W., Katz, H., Bjelic-Radisic, V., Ziefle, M.: Investigating paper vs. screen in real-life hospital workflows: Performance contradicts perceived superiority of paper in the user experience. International Journal of Human-Computer Studies 69(9), 563–570 (2011)CrossRefGoogle Scholar
  8. 8.
    Vogel, D., Baudisch, P.: Shift: a technique for operating pen-based interfaces using touch. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 657–666 (2007)Google Scholar
  9. 9.
    Holz, C., Baudisch, P.: Understanding touch. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, pp. 2501–2510 (2011)Google Scholar
  10. 10.
    Wigdor, D., Forlines, C., Baudisch, P., Barnwell, J., Shen, C.: Lucid touch: a see-through mobile device. In: Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology, pp. 269–278 (2007)Google Scholar
  11. 11.
    Baudisch, P., Chu, G.: Back-of-device interaction allows creating very small touch devices. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems, pp. 1923–1932 (2009)Google Scholar
  12. 12.
    Holzinger, A., Hoeller, M., Bloice, M., Urlesberger, B.: Typical Problems with developing mobile applications for health care: Some lessons learned from developing user-centered mobile applications in a hospital environment. In: Filipe, J., Marca, D.A., Shishkov, B., Sinderen, M.V. (eds.) International Conference on E-Business (ICE-B 2008), pp. 235–240. IEEE (2008)Google Scholar
  13. 13.
    Sokol, D.K., Hettige, S.: Poor handwriting remains a significant problem in medicine. Journal of the Royal Society of Medicine 99(12), 645–646 (2006)CrossRefGoogle Scholar
  14. 14.
    Gartner: Market Share: Mobile Communication Devices by Region and Country, 3Q11, http://www.gartner.com/resId=1847315 (last access: February 19, 2012)
  15. 15.
    Wang, F., Ren, X.S.: Empirical Evaluation for Finger Input Properties In Multi-touch Interaction. Assoc Computing Machinery, New York (2009)Google Scholar
  16. 16.
    Holzinger, A., Geierhofer, R., Searle, G.: Biometrical Signatures in Practice: A challenge for improving Human-Computer Interaction in Clinical Workflows. In: Heinecke, A.M., Paul, H. (eds.) Mensch & Computer: Mensch und Computer im Strukturwandel, Oldenbourg, pp. 339–347 (2006)Google Scholar
  17. 17.
    Lee, S.W.: Advances in Handwriting Recogntion. Series in Machine Perception and Artificial Intelligence (last access)Google Scholar
  18. 18.
    Holzinger, A., Schlögl, M., Peischl, B., Debevc, M.: Preferences of Handwriting Recognition on Mobile Information Systems in Medicine: Improving handwriting algorithm on the basis of real-life usability research (Best Paper Award). In: ICE-B 2010 - ICETE The International Joint Conference on e-Business and Telecommunications, pp. 120–123 (2010)Google Scholar
  19. 19.
    Holzman, T.G.: Computer-human interface solutions for emergency medical care. Interactions 6(3), 13–24 (1999)CrossRefGoogle Scholar
  20. 20.
    Anantharaman, V., Han, L.S.: Hospital and emergency ambulance link: using IT to enhance emergency pre-hospital care. International Journal of Medical Informatics 61(2-3), 147–161 (2001)CrossRefGoogle Scholar
  21. 21.
    Baumgart, D.C.: Personal digital assistants in health care: experienced clinicians in the palm of your hand? The Lancet 366(9492), 1210–1222 (2005)CrossRefGoogle Scholar
  22. 22.
    Chittaro, L., Zuliani, F., Carchietti, E.: Mobile Devices in Emergency Medical Services: User Evaluation of a PDA-Based Interface for Ambulance Run Reporting. In: Löffler, J., Klann, M. (eds.) Mobile Response 2007. LNCS, vol. 4458, pp. 19–28. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Holzinger, A., Errath, M.: Mobile computer Web-application design in medicine: some research based guidelines. Universal Access in the Information Society International Journal 6(1), 31–41 (2007)CrossRefGoogle Scholar
  24. 24.
    Holzinger, A., Basic, L., Peischl, B., Debevc, M.: Handwriting Recognition on Mobile Devices: State of the art technology, usability and business analysis. In: Proceedings of the 8th International Conference on Electronic Business and Telecommunications, INSTICC, pp. 219–227 (2011)Google Scholar
  25. 25.
    Klann, M., Malizia, A., Chittaro, L., Cuevas, I.A., Levialdi, S.: HCI for emergencies. In: CHI 2008 Extended Abstracts on Human Factors in Computing Systems, pp. 3945–3948 (2008)Google Scholar
  26. 26.
    Lewis, J.R.: Hfes, Input rates and user preference for three small-screen input methods: Standard keyboard, predictive keyboard, and handwriting. In: Proceedings of the Human Factors and Ergonomics Society 43rd Annual Meeting. Human Factors and Ergonomics Soc., vol. 1 and 2, pp. 425–428 (1999)Google Scholar
  27. 27.
    Haller, G., Haller, D.M., Courvoisier, D.S., Lovis, C.: Handheld vs. Laptop Computers for Electronic Data Collection in Clinical Research: A Crossover Randomized Trial. Journal of the American Medical Informatics Association 16(5), 651–659 (2009)CrossRefGoogle Scholar
  28. 28.
    Perwej, Y., Chaturvedi, A.: Machine recognition of Hand written Characters using neural networks. International Journal of Computer Applications 14(2), 6–9 (2011)CrossRefGoogle Scholar
  29. 29.
    Plotz, T., Fink, G.A.: Markov models for offline handwriting recognition: a survey. International Journal on Document Analysis and Recognition 12(4), 269–298 (2009)CrossRefGoogle Scholar
  30. 30.
    Graves, A., Schmidhuber, J.: Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks, http://www.idsia.ch/~juergen/nips2009.pdf (last access: February 17, 2011)
  31. 31.
    Sulong, G., Rehman, A., Saba, T.: Improved Offline Connected Script Recognition Based on Hybrid Strategy. International Journal of Engineering Science and Technology 2(6), 1603–1611 (2010)Google Scholar
  32. 32.
    Liu, Z., Cai, J., Buse, R.: Handwriting Recognition: Soft Computing and Probabilistic Approaches. Springer, New York (2003)MATHGoogle Scholar
  33. 33.
    Dzulkifli, M., Muhammad, F., Razib, O.: On-Line Cursive Handwriting Recognition: A Survey of Methods and Performance. In: The 4th International Conference on Computer Science and Information Technology, CSIT 2006 (2006)Google Scholar
  34. 34.
    Plamondon, R., Srihari, S.N.: On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 63–84 (2000)CrossRefGoogle Scholar
  35. 35.
    Shu, H.: On-Line Handwriting Recognition Using Hidden Markov Models, http://dspace.mit.edu/bitstream/handle/1721.1/42603/37145316.pdf (last access: February 18, 2011)
  36. 36.
    Zafar, M.F., Mohamad, D., Othman, R.M.: On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net. Journal of the Academy of Science, Engineering and Technology 10, 232–237 (2005), http://www.waset.org/journals/waset/v10/v10-44.pdf Google Scholar
  37. 37.
    Zafar, M.F., Mohamad, D., Othman, R.: Neural Nets for On-line Isolated Handwritten Character Recognition: A Comparative Study. In: The IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006 (2006)Google Scholar
  38. 38.
    Gowan, W.: Optical Character Recognition using Fuzzy Logic, http://www.freescale.com/files/microcontrollers/doc/app_note/AN1220_D.pdf (last access: February 18, 2011)
  39. 39.
    Gader, P.D., Keller, J.M., Krishnapuram, R., Chiang, J.H., Mohamed, M.A.: Neural and fuzzy methods in handwriting recognition. Computer 30(2), 79–86 (1997)CrossRefGoogle Scholar
  40. 40.
    Phatware: Calligrapher SDK 6.0 Developer’s Manual (2008)Google Scholar
  41. 41.
    Pittman, J.A.: Handwriting Recognition: Tablet PC Text Input. IEEE Computer 40(9), 49–54 (2007)CrossRefGoogle Scholar
  42. 42.
    Willis, N.: CellWriter: Open source handwriting recognition for Linux, http://www.linux.com/archive/feed/120867 (last access: February 18, 2011)
  43. 43.
    VisionObjects: MyScript Stylus, http://www.visionobjects.com/handwriting_recognition/DS_MyScript_Stylus_3.0.pdf (last access: February 15, 2011)
  44. 44.
    Castellucci, S.J., MacKenzie, I.S.: Acm: Graffiti vs. Unistrokes: An Empirical Comparison. Assoc Computing Machinery, New York (2008)Google Scholar
  45. 45.
    Sears, A., Arora, R.: Data entry for mobile devices: an empirical comparison of novice performance with Jot and Graffiti. Interacting with Computers 14(5), 413–433 (2002)CrossRefGoogle Scholar
  46. 46.
    Holzinger, A.: Finger Instead of Mouse: Touch Screens as a Means of Enhancing Universal Access. In: Carbonell, N., Stephanidis, C. (eds.) UI4ALL 2002. LNCS, vol. 2615, pp. 387–397. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  47. 47.
    Neisser, U., Weene, P.: A note on human recognition of hand-printed characters. Information and Control 3, 191–196 (1960)CrossRefGoogle Scholar
  48. 48.
    Kwon, S., Lee, D., Chung, M.K.: Effect of key size and activation area on the performance of a regional error correction method in a touch-screen QWERTY keyboard. International Journal of Industrial Ergonomics 39(5), 888–893 (2009)CrossRefGoogle Scholar
  49. 49.
    Koskinen, E., Kaaresoja, T., Laitinen, P.: Feel-good touch: finding the most pleasant tactile feedback for a mobile touch screen button. In: Proceedings of the 10th International Conference on Multimodal Interfaces, pp. 297–304 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andreas Holzinger
    • 1
  • Gig Searle
    • 1
  • Bernhard Peischl
    • 2
  • Matjaz Debevc
    • 3
  1. 1.Institute for Medical Informatics, Statistics and Documentation, Research Unit HCI4MEDMedical University GrazGrazAustria
  2. 2.Softnet AustriaGrazAustria
  3. 3.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia

Personalised recommendations