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Recognizing handwritten text

Published:01 March 1991Publication History
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References

  1. 1.Alien, R. B. Connectionist language users. Connection Science, 2(4), 1990.Google ScholarGoogle Scholar
  2. 2.Badie, K. and Shimura, M. Machine recognition of roman cursive scripts. 1982, pp. 28-30.Google ScholarGoogle Scholar
  3. 3.Brown, M. K. and Ganapathy, S. Cursive script recognition. Fifth International Conference on Character Recognition, 1980, pp. 47-51.Google ScholarGoogle Scholar
  4. 4.Buell, B. and Brandt, R. The pen: Computing's next big leap. Business Week, May 14, 1990.Google ScholarGoogle Scholar
  5. 5.Derthick, M. The minimum description length principle applied to feature learning and analogical mapping. MCC technical report ACT-CYC-234-90, 1990.Google ScholarGoogle Scholar
  6. 6.Earnest, L. D. Machine recognition of cursive writing. Information Processing 1962. Amsterdam: North Holland, 1963, pp. 462-466.Google ScholarGoogle Scholar
  7. 7.Eden, M. Handwriting and pattern recognition. IRE Translations on Information Theory, February, 1962, pp. 160-166.Google ScholarGoogle ScholarCross RefCross Ref
  8. 8.Eden, M. and Halle, M. The characterization of cursive writing. In C. Cherry (ed.), Proceedings of the Fourth London Symposium on Information Theory. London, England: Butterworths, 1961, pp. 287-299.Google ScholarGoogle Scholar
  9. 9.Ehrich, R. W. and Koehler, K.J. Experiments in the contextual recognition of cursive script. IEEE Transactions on Computers, Vol C-24 (2), February, 1975.Google ScholarGoogle Scholar
  10. 10.Frishkopf, L. S. and Harmon, L. D. Machine reading of cursive script. In C. Cherry (ed.), Proceedings of the Fourth London Symposium on Information Theory, London: Butterworth, 1961, pp 300-315.Google ScholarGoogle Scholar
  11. 11.Guyon, I., Albrecht, P., LeCun, Y., Denker, J., and Hubbard, W. Design of a neural network character recognizer for a touch terminal. Pattern Recognition, 24(2), 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. 12.Hinton, G. E. Learning distributed representations of concepts. Proceedings of the Eighth Annual Conference of The Cognitive Science Society, Amherst, Massachusetts: Lawrence Erlbaum Associates, 1986.Google ScholarGoogle Scholar
  13. 13.Hinton, G. E., McClelland, J. L., and Rumelhart, D. E. Distributed representations. In D. E. Rumelhart and J. L. McClelland (eds.), Parallel Distributed Processing. Cambridge, Massachusetts: The MIT Press, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. 14.LeCun, Y. Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D. Handwritten digit recognition with a back-propagation network. In Touretzky, D. S., (ed.)Neural Information Processing Systems 2, Morgan Kaufmann, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. 15.Martin, G. L. Using neural networks to recognize hand-drawn symbols. MCC technical report ACT-HI- 232-90, 1990.Google ScholarGoogle Scholar
  16. 16.Martin, G. L. Integrating segmentation and recognition stages for overlapping hand-printed characters. MCC technical report ACT -NN -3 20 -9 0.Google ScholarGoogle Scholar
  17. 17.Martin, G. L., Leow, W. K., and Pittman, J. A. Function complexity effects on back-propagation learning. MCC technical report ACT-HI-062-90, 1990.Google ScholarGoogle Scholar
  18. 18.Martin, G. L. and Pittman J. A. Recognizing handprinted letters and digits. In Touretzky, D. S., (ed.) Neural information Processing Systems 2, Morgan Kaufmann, 1990. Also available as MCC technical report ACA-HI-017-90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. 19.Martin, G. L. and Pittman J. A. Recognizing handprinted letters and digits using backpropagation learning. Neural Computation, 3(2), 1991, in press.Google ScholarGoogle Scholar
  20. 20.Martin, G. L., Pittman, J. A., Wittenburg, K., Cohen, R., and Parish, T. Sign here, Please. BYTE, July, 1990, 243-251. Also available as MCC technical report ACT-HI- 199-90.Google ScholarGoogle Scholar
  21. 21.McClelland, J. L and Kawamoto, A.H. Mechanisms of sentence processing: Assigning roles to constituents. In D. E. Rumelhart and J. L. McClelland (eds.), Parallel Distributed Processing. Cambridge, Massachusetts: The MIT Press, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. 22.Mori, Y. and Joe, K. A large-scale neural network which recognizes handwritten Kanji characters. In Touretzky, D. S., (ed.) Neural information Processing Systems 2, Morgan Kaufmann, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. 23.Pollack, J. and Waltz, D. L. Interpretation of natural language. Byte, February, 1986.Google ScholarGoogle Scholar
  24. 24.Rumelhart, D. E., Hinton, G. E., and Williams, R. J. Learning internal representations by error propagation. In D. E. Rumelhart and J. L. McClelland (eds.), Parallel Distributed Processing. Cambridge, Massachusetts: The MIT Press, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. 25.Rumelhart, D. E. and McClelland, j.L. On learning the past tenses of English verbs. In D. E. Rumelhart and J. L. McClelland (eds.), Parallel Distributed Processing. Cambridge, Massachusetts: The MIT Press, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. 26.Sejnowski, T. J. and Rosenberg, C.R. NetTalk: A parallel network that learns to read aloud. Johns Hopkins University Electrical Engineering and Computer Science technical report JHU/EECS-86/01, 1986.Google ScholarGoogle Scholar
  27. 27.Sexton, T. Go's PenPoint redefines handwriting recognition. PC Week, August 20, 1990.Google ScholarGoogle Scholar
  28. 28.Shannon, C. E. Prediction and entropy of printed English. Bell Systems Technical Journal, Vol 30, January, 1951.Google ScholarGoogle ScholarCross RefCross Ref
  29. 29.Shannon, C. E. and Weaver, W. The Mathematical Theory of Communication. Urbana, Illinois: University of Illinois Press, 1963, p. 13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. 30.Talley, j. Personal communication. 1989.Google ScholarGoogle Scholar
  31. 31.Tappert, C. C. Cursive script recognition by elastic matching. IBM Journal of Research and Development, November, 1982, 26(6), 765-771.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. 32.Tappert, C. C., Suen, C. Y., and Wakahara, T. The state of the art in on-line handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(8), August, 1990, pp. 787-808. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. 33.Touretzky, D. S. and Derthick, M. A. Symbol processing in connectionist networks" Five properties and two architectures. Proceedings of the IEEE Spring COMP-CON87, San Francisco, 1987.Google ScholarGoogle Scholar
  34. 34.Touretzky, D. S. and Elvgren, G. Rule representation in a connectionist chunker. In Touretzky, D. S., (ed.) Neural Information Processing Systems 2, Morgan Kaufmann, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. 35.Touretzky, D. S. and Geva, S. A distributed connectionist representation for concept structures. Proceedings of the Ninth Annual Conference of The Cognitive Science Society, Seattle, Washington: Lawrence Erlbaum Associates, 1987.Google ScholarGoogle Scholar
  36. 36.Ward, R.R. Annotated bibliography of topics relating to on-line handwriting input. Lowell, Massachusetts" Computer Annotation Technologies Group, Wang Laboratories.Google ScholarGoogle Scholar
  37. 37.Wieland, A. and Leighton, R. Shaping schedules as a method for accelerated learning. Proceedings of the First International Neural Network Society Conference, 1988.Google ScholarGoogle ScholarCross RefCross Ref
  38. 38.Zachary P. G. Computer firms see the writing on the screen. Wall Street Journal, April 30, 1990.Google ScholarGoogle Scholar

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          cover image ACM Conferences
          CHI '91: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
          April 1991
          511 pages
          ISBN:0897913833
          DOI:10.1145/108844

          Copyright © 1991 ACM

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          • Published: 1 March 1991

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