HANDWRITING RECOGNITION ON MOBILE DEVICES - State of the Art Technology, Usability and Business Analysis

Andreas Holzinger, Lamija Basic, Bernhard Peischl, Matjaz Debevc

Abstract

The software company FERK-Systems has been providing mobile health care information systems for various German medical services (e.g. Red Cross) for many years. Since handwriting is an issue in the medical and health care domain, a system for handwriting recognition on mobile devices has been developed within the last few years. While we have been continually improving the degree of recognition within the system, there are still changes necessary to ensure the reliability that is imperative in this critical domain. In this paper, we present the major improvements made since our presentation at the ICE-B 2010, along with a recent real-life usability evaluation. Moreover, we discuss some of the advantages and disadvantages of current systems, along with some business aspects of the vast, and growing, mobile handwriting recognition market.

References

  1. Dzulkifli, M., Muhammad, F. & Razib, O. (2006) On-Line Cursive Handwriting Recognition: A Survey of Methods and Performance. The 4th International Conference on Computer Science and Information Technology (CSIT2006). Amman, Jordan 5-7 April, 2006.
  2. Gader, P. D., Keller, J. M., Krishnapuram, R., Chiang, J. H. & Mohamed, M. A. (1997) Neural and fuzzy methods in handwriting recognition. Computer, 30, 2, 79-86.
  3. Gowan, W. (2004), Optical Character Recognition using Fuzzy Logic. Online available: http://www.freescale. com/files/microcontrollers/doc/app_note/AN1220_D.p df, last access: 2011-02-18
  4. Graves, A. & Schmidhuber, J. (2009), Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. Online available: http://www.idsia.ch/ juergen/nips2009.pdf, last access: 2011-02-17
  5. Holzinger, A. (2003) Finger Instead of Mouse: Touch Screens as a means of enhancing Universal Access. In: Carbonell, N. & Stephanidis, C. (Eds.) Universal Access: Theoretical Perspectives, Practice and Experience, Lecture Notes in Computer Science (LNCS 2615) Berlin, Heidelberg, New York, Springer, 387-397.
  6. Holzinger, A., Geierhofer, R. & Searle, G. (2006) 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. München, Oldenbourg, 339-347.
  7. Holzinger, A., Hoeller, M., Bloice, M. & Urlesberger, B. (2008a). Typical Problems with developing mobile applications for health care: Some lessons learned from developing user-centered mobile applications in a hospital environment. International Conference on E-Business (ICE-B 2008), Porto (PT), IEEE, 235-240.
  8. Holzinger, A., Höller, M., Schedlbauer, M. & Urlesberger, B. (2008b). An Investigation of Finger versus Stylus Input in Medical Scenarios. ITI 2008: 30th International Conference on Information Technology Interfaces, Cavtat, Dubrovnik, IEEE, 433-438.
  9. Holzinger, A., Schlögl, M., Peischl, B. & Debevc, M. (2010) Preferences of Handwriting Recognition on Mobile Information Systems in Medicine: Improving handwriting algorithm on the basis of real-life usability research (Best Paper Award). ICE-B 2010 - ICETE The International Joint Conference on eBusiness and Telecommunications. Athens (Greece), INSTICC.
  10. Lee, S. W. (1999), Advances in Handwriting Recogntion (Series in Machine Perception and Artificial Intelligence. Online available, last access:
  11. Liu, Z., Cai, J. & Buse, R. (2003) Handwriting Recognition: Soft Computing and Probabilistic Approaches. New York, Springer.
  12. Perwej, Y. & Chaturvedi, A. (2011) Machine recognition of Hand written Characters using neural networks. International Journal of Computer Applications, 14, 2, 6-9.
  13. Phatware (2008) Calligrapher SDK 6.0 Developer's Manual.
  14. Pittman, J. A. (2007) Handwriting Recognition: Tablet PC Text Input. IEEE Computer, 40, 9, 49-54.
  15. Plamondon, R. & Srihari, S. N. (2000) On-Line and OffLine Handwriting Recognition: A Comprehensive Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1, 63-84.
  16. Plotz, T. & Fink, G. A. (2009) Markov models for offline handwriting recognition: a survey. International Journal on Document Analysis and Recognition, 12, 4, 269-298.
  17. Shu, H. (1997), On-Line Handwriting Recognition Using Hidden Markov Models. Online available: http://dspace.mit.edu/bitstream/handle/1721.1/42603/3 7145316.pdf, last access: 2011-02-18
  18. Sulong, G., Rehman, A. & Saba, T. (2010) Improved Offline Connected Script Recognition Based on Hybrid Strategy. International Journal of Engineering Science and Technology, 2, 6, 1603-1611.
  19. VisionObjects (2009), MyScript Stylus. Online available: http://www.visionobjects.com/handwriting_recognitio n/DS_MyScript_Stylus_3.0.pdf, last access: 2011-02- 15
  20. Wang, F. & Ren, X. S. (2009) Empirical Evaluation for Finger Input Properties In Multi-touch Interaction. New York, Assoc Computing Machinery.
  21. Willis, N. (2007), CellWriter: Open source handwriting recognition for Linux, Online: . Online available: http://www.linux.com/archive/feed/120867, last access: 2011-02-18
  22. Yaeger, L. S., Fabrick, R. W. & Pagallo, G. M. (2009) Method and Apparatus for Acquiring and Organizing Ink Information in Pen-Aware Computer Systems 20090279783.
  23. Zafar, M. F., Mohamad, D. & Othman, R. (2006) Neural Nets for On-line Isolated Handwritten Character Recognition: A Comparative Study. The IEEE International Conference on Engineering of Intelligent Systems (ICEIS 2006). Islamabad, 22-23 April 2006.
  24. Zafar, M. F., Mohamad, D. & Othman, R. M. (2005) Online Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net. Journal of the Academy of Science, Engineering and Technology (Available online: http://www.waset. org/journals/waset/v10/v10-44.pdf), 10, 232-237.
Download


Paper Citation


in Harvard Style

Holzinger A., Basic L., Peischl B. and Debevc M. (2011). HANDWRITING RECOGNITION ON MOBILE DEVICES - State of the Art Technology, Usability and Business Analysis . In Proceedings of the International Conference on e-Business - Volume 1: ICE-B, (ICETE 2011) ISBN 978-989-8425-70-6, pages 219-227. DOI: 10.5220/0003522102190227


in Bibtex Style

@conference{ice-b11,
author={Andreas Holzinger and Lamija Basic and Bernhard Peischl and Matjaz Debevc},
title={HANDWRITING RECOGNITION ON MOBILE DEVICES - State of the Art Technology, Usability and Business Analysis},
booktitle={Proceedings of the International Conference on e-Business - Volume 1: ICE-B, (ICETE 2011)},
year={2011},
pages={219-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003522102190227},
isbn={978-989-8425-70-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on e-Business - Volume 1: ICE-B, (ICETE 2011)
TI - HANDWRITING RECOGNITION ON MOBILE DEVICES - State of the Art Technology, Usability and Business Analysis
SN - 978-989-8425-70-6
AU - Holzinger A.
AU - Basic L.
AU - Peischl B.
AU - Debevc M.
PY - 2011
SP - 219
EP - 227
DO - 10.5220/0003522102190227