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On-line cursive script recognition using time-delay neural networks and hidden Markov models

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Abstract

We present a writer-independent system for online handwriting recognition that can handle a variety of writing styles including cursive script and handprinting. The input to our system contains the pen trajectory information, encoded as a time-ordered sequence of feature vectors. A time-delay neural network is used to estimate a posteriori probabilities for characters in a word. A hidden Markov model segments the word in a way that optimizes the global word score, using a dictionary in the process. A geometrical normalization scheme and a fast but efficient dictionary search are also presented. Trained on 20 k words from 59 writers, using a 25 k-word dictionary, our system reached recognition rates of 89% for characters and 80% for words on test data from a disjoint set of writers.

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References

  1. Bengio Y, LeCun Y, Henderson D (1994) Globally trained handwritten word recognizer using spatial representation, convolutional neural networks and hidden markov models. In: Cowan JD, Tesauro G, Alspector J (eds) Advances in neural information processing systems 6. Morgan Kauffman, Denver, Colo., pp 937–944

    Google Scholar 

  2. Berthod M (1990) On-line analysis of cursive writing. In: Suen CY, Mori RD (eds) Computer analysis and perception: Vol. 1 — Visual Signals, CRC Press, Boca Raton, Florida, pp 55–81

    Google Scholar 

  3. Bourlard H, Wellekens C (1990) Links between markov models and multilayer perception. IEEE Trans Patt Anal Machine Intell 12:1167–1178

    Google Scholar 

  4. Bourlard H, Morgan N (1993) Connectionist speech recognition. Kluver Academic, Boston, MA

    Google Scholar 

  5. Enrich RW, Koehler KJ (1975) Experiments in the contextual recognition of cursive script. IEEE Trans Comput 24:182–194

    Google Scholar 

  6. Fujisaka T, Nathan K, Cho W, Beigi H (1993) On-line unconstrained handwriting recognition by a probabilistic method. International Workshop on Frontiers in Handwriting Recognition III. Buffalo, N.Y., pp 231–241

  7. Gupta VN, Lennig M, Mermelstein P (1988) Fast search strategy in a large vocabulary word recognizer. J Acoust Soc Am 84:2007–2017

    Google Scholar 

  8. Guyon I, Albrecht P, Cun YL, Denker J, Hubbard W (1991) Design of a neural network character recognizer for a touch terminal. Patt Recogn 24:105–119

    Google Scholar 

  9. Ha JY, Oh SC, Kim JH, Kwon YB (1993) Unconstrained handwritten word recognition with interconnected hidden markov models. International Workshop on Frontiers in Handwriting Recognition III, Buffalo, N.Y., pp 455–460

  10. Harmon LD (1962) Automatic reading of cursive script. In: Fisher GL (ed) Optical character recognition, Washington, DC, pp 151–152(A)

  11. Huang WY, Lippmann RP (1990) HMM speech recognition with neural net discrimination. In: Touretzky DS (ed) Advances in neural information processing systems 2, Morgan Kaufmann, Denver, Colo., pp 194–202

    Google Scholar 

  12. Keeler J, Rumelhart DE, Leow WK (1991) Integrated segmentation and recognition of hand-printed numerals In: Lippmann RP, Moody JE, Touretzky DS (eds) Advances in neural information processing systems 3, Morgan Kaufmann, Denver, Colo., pp 557–563

    Google Scholar 

  13. Lang KJ, Hinton G (1988) A time delay neural network architecture for speech recognition. Technical Report CMU-cs-88-152, Carnegie-Mellon University, Pittsburgh, Pa

    Google Scholar 

  14. Lecolinet E, Baret O (1993) Cursive word recognition: methods and strategies. NATO-ASI summer school, fundamentals in Handwriting Recognition, Bonas, France, 1993 July, preprint

  15. LeCun Y (1989) Generalization and network design strategies. In: Pfeifer R, Schreter Z, Fogelman F, Steels L (eds) Connectionism in perspective, Elsevier Zurich

    Google Scholar 

  16. Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics-Doklady, 10:707–710

    Google Scholar 

  17. Matan O, Burges CJC, LeCun Y, Denker J (1992) Multidigit recognition using a space dispacement neural network. In: Moody JE, Hanson SJ, Lippmann RP (eds) Advances in neural information processing systems 4, Morgan Kaufmann, Denver, Colo., pp 488–495

    Google Scholar 

  18. Mermelstein P, Eden M (1964) Experiments on computer recognition of connected handwritten words. Information and Control 7:255

    Google Scholar 

  19. Morasso P, Barberis L, Pagliano S, Vergano D (1993) Recognition experiments of cursive dynamic handwriting with self-organizing networks. Patt Recogn 26:451–460

    Google Scholar 

  20. Nag R, Wong KH, Fallside F (1986) Script recognition using hidden Markov models. IEEE International Conference on Acoustics, Speech, and Signal Processing, Tokyo, pp 2071–2074

  21. Okuda T, Tanaka E, Tamotsu K (1976) A method for the correction of garbled words based on the Levenshtein metric. IEEE Trans Comput 25:172–177

    Google Scholar 

  22. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE:77:257–285

    Google Scholar 

  23. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstructure of cognition, volume I, Bradford Books, Cambridge, Mass., pp 318–362

    Google Scholar 

  24. Sayre KM (1973) Machine recognition of handwritten words: a project report. Patt Recogn 5:213–228

    Google Scholar 

  25. Schenkel M, Weissman H, Guyon I, Nohl C, Henderson D (1993) Recognition-based segmentation of on-line hand-printed words. In: Hanson SJ, Cowan JD, Giles CL (eds) Advances in neural information processing systems 5, Morgan Kaufmann, Denver, Colo., pp 723–730

    Google Scholar 

  26. Schomaker L (1993) Using stroke- or character-based self-organizing maps in the recognition of on-line, connected cursive script. Patt Recogn 26:443–450

    Google Scholar 

  27. Tappert CC, Suen CY, Wakahara T (1990) The state of the art in on-line handwriting recognition. IEEE Trans Patt Anal Machine Intell 12:787–808

    Google Scholar 

  28. Waibel A, Hanazawa T, Hinton G, Shikano K, Lang K (1989) Phoneme recognition using time-delay neural networks. IEEE Trans Acoustics Speech Signal Processing 37:328–339

    Google Scholar 

  29. Weissman H, Schenkel M, Guyon I, Nohl C, Henderson D (1994) Recognition-based segmentation of on-line handprinted words: input vs. output segmentation. Patt Recogn 27:405–420

    Google Scholar 

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Schenkel, M., Guyon, I. & Henderson, D. On-line cursive script recognition using time-delay neural networks and hidden Markov models. Machine Vis. Apps. 8, 215–223 (1995). https://doi.org/10.1007/BF01219589

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