Real-time Processing of Cursive Writing and Sketched Graphics

  • Arnold J. W. M. Thomassen
  • Hans-Leo Teulings
  • Lambert R. B. Schomaker
Conference paper

Abstract

The advances that have recently been made with respect to intelligent workstations and software also involve highly sophisticated recognition algorithms. The latter open up attractive possibilities for accessing the computer by means of the “natural” linguistic communication modes of speech and writing. Yet the large-scale introduction of keyboard-and-screen text editors with the many human-machine interaction problems associated with that revolution, and the spectacular potential of speech processing soliciting huge and lasting research investments in that area, seem to be responsible for the relative oblivion of the study of handwriting and drawing as efficient modes of human-computer interaction. The present contribution intends to point out the attractiveness and feasibility of using pen and paper as a natural communication device in an office work environment.

Keywords

Fatigue Editing unIver Cute Ambi 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ali F. & Pavlidis Th. (1977). Syntactic recognition of handwritten numerals. IEEE Transactions on Systems, Man and Cybernetics, SMC-7, pp. 537–541.CrossRefGoogle Scholar
  2. Apsey R.S. (1978). Human factors of constrained handprint for OCR. IEEE Transactions on Systems, Man and Cybernetics. SMC-8, pp. 292–296.Google Scholar
  3. Belaid A. & Haton J.-P. (1984). A syntactic approach for handwritten mathematical formula recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6, pp. 105–111.CrossRefGoogle Scholar
  4. Dainoff M.J. (1982), Occupational stress factors in visual display terminal (VDT) operation: A review of empirical research. Behavior and Information Technology, 1, pp. 141–176.CrossRefGoogle Scholar
  5. Denier van der Gon J.J., Thuring J.Ph. & Strackee J. (1962). A handwriting simulator. Physics in Medicine and Biology, 6, pp. 407–413.CrossRefGoogle Scholar
  6. Desain P. (1988). Treedoctor: A package for animation and manipulation of tree structures. In: Human-Computer Interaction: Psychonomic Aspects. G.C. van der Veer & G. Mulder (Eds.). Springer, Heidelberg.Google Scholar
  7. Dooijes E.H. (1984). Analysis of handwriting movements. Ph.D. Thesis, University of Amsterdam. Krips Repro, Meppel.Google Scholar
  8. Earnest L.D. (1963). Machine recognition of cursive writing. In: Information processing 1962. C.M. Popplewell (Ed.)., North-Holland, Amsterdam, pp. 462–466.Google Scholar
  9. Eden M. & Halle M. (1961). The characterization of cursive writing. In: Information Theory. C. Cherry (Ed.), Butterworths, London, pp. 287–299.Google Scholar
  10. Embley D.W. & Nagy G. (1981). Behavioral aspects of text editors. Computing Surveys, 13, pp. 33–70.CrossRefGoogle Scholar
  11. Frishkopf L.S. & Harmon L.D. (1961). Machine reading of cursive script. In: Information Theory. C. Cherry (Ed.). Butterworths, London, pp. 300–316.Google Scholar
  12. Gould J.D. & Alfaro L. (1984). Revising documents with text editors, handwriting-recognition systems and speech-recognition systems. Human Factors, 26, pp. 391–406.Google Scholar
  13. Gould J.D. & Grichkowsky N. (1984). Doing the same work with hard copy and with cathode-ray tube (CRT) computer terminals. Human Factors, 26, pp. 323–337.Google Scholar
  14. Harmon L.D. (1972). Automatic recognition of print and script. Proceedings of the IEEE, 60, pp. 1165–1176.CrossRefGoogle Scholar
  15. Helander M.G., Billingsley P.A. & Schurick J.M. (1984). An evaluation of human factors research on visual display terminals in the workplace. Human Factors Review, 3, pp. 55–129.Google Scholar
  16. Himmel D.P. (1978). Some real-world experiences with handprinted optical character recognition. IEEE Transactions on Systems, Man, and Cybernetics. SMC-8, pp. 288–292.Google Scholar
  17. Kao H.S.R., Van Galen G.P. & Hoosain R. (Eds.). (1986). Graphonomics: Contemporary Research in handwriting. North-Holland, Amsterdam.Google Scholar
  18. Kruk R.S. & Muter P. (1984), Reading of continuous text on video screens. Human Factors, 26, pp. 339–345.Google Scholar
  19. Lin W.C. & Pun J.H. (1978). Machine recognition and plotting of hand-sketched line figures. IEEE Transactions on Systems, Man and Cybernetics. SMC-8, pp. 52–57.Google Scholar
  20. Lindgren N. (1965). Machine recognition of human language. Part III: Cursive script recognition. IEEE Spectrum, 2, pp. 104–116.CrossRefGoogle Scholar
  21. Maarse J.F., Schomaker L.R.B. & Teulings H.-L. (1988). Automatic identification of writers. In: Human-Computer Interaction: Psychonomic Aspects, G.C. van der Veer & G. Mulder (Eds.), Springer, Heidelberg.Google Scholar
  22. Mermelstein P. & Eden M. (1964). Experiments or computer recognition of connected handwritten words. Information and Control, 7, pp. 225–270.CrossRefGoogle Scholar
  23. Morasso P. & Mussa Ivaldi F.A. (1982). Trajectory formation and handwriting: A computational model. Biological Cybernetics, 45, pp. 131–142.CrossRefGoogle Scholar
  24. National Research Council (1983). Video displays, work and vision. National Academy Press, Washington.Google Scholar
  25. Padmos P., Pot F.D., Vos J.J. & De Vries-Mol, E.C. (1985). Gezondheid en welbevinden bij het werken met beeldschermen I: Report of a preliminary study. Report 8412139. Ministerie van Sociale Zaken en Werkgelegenheid, The Hague.Google Scholar
  26. Planmondon R. & Baron R. (1986). Handwritten interaction between a dedicated microcomputer and a software tool: System prototyping. Journal of Microcomputer Applications, 9. pp. 51–60.CrossRefGoogle Scholar
  27. Sayre K.M. (1973) Machine recognition of handwritten words: A project report. Pattern Recognition, 5, pp. 213–228.CrossRefGoogle Scholar
  28. Shneiderman B. (1983). Direct manipulation: A STEP beyond programming languages. IEEE Transactions on computers, 16, pp. 57–69.Google Scholar
  29. Spanjersberg A.A. (1978). Experiments with automatic input of handwritten numeric data into a large administrative system. IEEE Transactions on Systems, Man and Cybernetics. SMC-8, pp. 286–288.Google Scholar
  30. Suen C.Y., Berthold M. and Mori S. (1980). Automatic recognition of hand-printed characters — the state of the art. IEEE Proceedings, 68, pp. 468–487.CrossRefGoogle Scholar
  31. Suenaga Y. & Nagura M. (1980). A facsimile based manuscript layout and editing system by auxiliary mark recognition. Proceedings of the 5th International Conference on Pattern Recognition, IEEE Computer Society, pp. 856–858.Google Scholar
  32. Tappert C.G. (1986). An adaptive system for handwriting recognition. In: Graphonomics: Contemporary research in handwriting. H.S.R. Kao, G.P. van Galen & R. Hoosain (Eds.), North-Holland, Amsterdam.Google Scholar
  33. Teulings H.-L., Schomaker L.R.B. & Thomassen A.J.W.M. (1986). Compatible software library for low-level processing of cursive script. Esprit Report TK3-WP1-DI1.Google Scholar
  34. Thomassen A.J.W.M., Keuss PJ.G. & Van Galen G.P. (Eds.). (1984). Motor aspects of handwriting: Approaches to movement in graphic behavior. North-Holland, Amsterdam.Google Scholar
  35. Vredenbregt J. & Koster W.G. (1971). Analysis and synthesis of handwriting. In: Philips Technical Review, 32, pp. 73–78.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Arnold J. W. M. Thomassen
  • Hans-Leo Teulings
  • Lambert R. B. Schomaker

There are no affiliations available

Personalised recommendations