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The Writer Independent Online Handwriting Recognition System frog on hand and Cluster Generative Statistical Dynamic Time Warping

March 2004 (vol. 26 no. 3)

pp. 299-310

[1] 299 L.R. Bahl, F. Jelinek, and R.L. Mercer, A Maximum Likelihood Approach to Continuous Speech Recognition IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 5, pp. 179-190, Mar. 1983.[2] C. Bahlmann, Directional Features in On-Line Handwriting Recognition technical report, Albert-Ludwigs-Universität Freiburg, Institut für Informatik,http:/lmb.informatik.uni- freiburg.de/, 2003.[3] C. Bahlmann, Advanced Sequence Classification Techniques Applied to On-Line Handwriting Recognition PhD thesis, Albert-Ludwigs-Universität Freiburg, Institut für Informatik, to appear,http:/lmb.informatik.uni-freiburg.de/, 2004.[4] C. Bahlmann and H. Burkhardt, Measuring HMM Similarity with the Bayes Probability of Error and Its Application to Online Handwriting Recognition Proc. Sixth Int'l Conf. Document Analysis and Recognition, pp. 406-411, 2001.[5] C. Bahlmann, B. Haasdonk, and H. Burkhardt, On-Line Handwriting Recognition with Support Vector Machines A Kernel Approach Proc. Eighth Int'l Workshop Frontiers in Handwriting Recognition, pp. 49-54, 2002.[6] S. Bercu and G. Lorette, On-Line Handwritten Word Recognition: An Approach Based On Hidden Markov Models Proc. Third Int'l Workshop Frontiers in Handwriting Recognition, pp. 385-390, 1993.[7] S.D. Connell and A.K. Jain, Learning Prototypes for On-Line Handwriting Digits Proc. 12th Int'l Conf. Pattern Recognition, pp. 182-184, 1994.[8] A. Dengel, R. Hoch, F. Hönes, T. Jäger, M. Malburg, and A. Weigel, Techniques for Improving OCR Results Handbook of Character Recognition and Document Image Analysis, H. Bunke and P.S.P. Wang, eds., pp. 227-258, World Scientific, 1997.[9] N. Gauthier, T. Artières, B. Dorizzi, and P. Gallinari, Strategies for Combining On-Line and Off-Line Information in an On-Line Handwriting Recognition System Proc. Sixth Int'l Conf. Document Analysis and Recognition, pp. 412-416, 2001.[10] W. Guerfali and R. Plamondon, Normalizing and Restoring On-Line Handwriting Pattern Recognition, vol. 16, no. 5, 1993.[11] I. Guyon, P. Albrecht, Y. LeCun, J.S. Denker, and W. Hubbard, Design of a Neural Network Character Recognizer for a Touch Terminal Pattern Recognition, vol. 24, no. 2, pp. 105-119, 1991.[12] I. Guyon, L.R.B. Schomaker, R. Plamondon, and R. Liberman, “UNIPEN Project of On-Line Data Exchange and Recognizer Benchmarks,” Proc. 12th Int'l Conf. Pattern Recognition, pp. 29-33, 1995.[13] J. Hu, S.G. Lim, and M.K. Brown, Writer Independent On-Line Handwriting Recognition Using an HMM Approach Pattern Recognition, vol. 33, pp. 133-147, Jan. 2000.[14] T. Jaakkola, M. Diekhans, and D. Haussler, Using the Fisher Kernel Method to Detect Remote Protein Homologies Proc. Eighth Int'l Conf. Image Analysis and Processing, 1999.[15] S. Jäger, S. Manke, and A. Waibel, NPEN++: An On-Line Handwriting Recognition System Proc. Seventh Int'l Workshop Frontiers in Handwriting Recognition, pp. 249-260, 2000.[16] F. Jelinek, Statistical Methods for Speech Recognition. MIT Press, 1998.[17] B.H. Juang, W. Chou, and C.H. Lee, Minimum Classification Error Rate Methods for Speech Recognition IEEE Trans. Speech&Audio Processing, vol. 5, no. 3, pp. 257-265, May 1997.[18] B.H. Juang and L.R. Rabiner, The Segmental k-Means Algorithm for Estimating Parameters of Hidden Markov Models IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 38, pp. 1639-1641, 1990.[19] J.J. Lee, J. Kim, and J.H. Kim, Data Driven Design of HMM Topology for On-Line Handwriting Recognition Proc. Seventh Int'l Workshop Frontiers in Handwriting Recognition, pp. 239-248, 2000.[20] S. Manke, M. Finke, and A. Waibel, A Fast Search Technique for Large Vocabulary On-Line Handwriting Recognition Proc. Fifth Int'l Workshop Frontiers in Handwriting Recognition, 1996.[21] T. Oates, L. Firoiu, and P. Cohen, Clustering Time Series with Hidden Markov Models and Dynamic Time Warping Proc. IJCAI-99 Workshop Neural, Symbolic, and Reinforcement Learning Methods for Sequence Learning, pp. 17-21, 1999.[22] T. Oates, M.D. Schmill, and P. Cohen, A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments Proc. 17th Nat'l Conf. Artificial Intelligence, pp. 846-851, 2000.[23] M. Parizeau, A. Lemieux, and C. Gagnè, Character Recognition Experiments Using UNIPEN Data Proc. Sixth Int'l Conf. Document Analysis and Recognition, pp. 481-485, 2001.[24] M.P. Perrone and S.D. Connell, K-Means Clustering for Hidden Markov Models Proc. Seventh Int'l Workshop Frontiers in Handwriting Recognition, pp. 229-238, 2000.[25] R. Plamondon, D. Lopresti, L.R.B. Schomaker, and R. Srihari, On-Line Handwriting Recognition Encyclopedia of Electrical and Electronics Eng., J.G. Webster, ed., vol. 15, pp. 123-146, Wiley InterScience, 1999.[26] R. Plamondon and S.N. Srihari, On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, Jan. 2000.[27] L. Prevost and M. Milgram, Non-Supervised Determination of Allograph Sub-Classes for On-Line Omni-Scriptor Handwriting Recognition Proc. Fifth Int'l Conf. Document Analysis and Recognition, 1999.[28] L.R. Rabiner and B.H. Juang, Fundamentals of Speech Recognition. Prentice Hall, 1993.[29] G. Rigoll, A. Kosmala, and D. Willett, “A New Hybrid Approach to Large Vocabulary Cursive Handwriting Recognition,” Proc. 14th Int'l Conf. Pattern Recognition, vol. 2, pp. 1,512-1,514, Brisbane, Australia, Aug. 1998.[30] M. Schenkel, I. Guyon, and D. Henderson, On-Line Cursive Script Recognition Using Time Delay Neural Networks and Hidden Markov Models Machine Vision and Applications 8, R. Plamondon, ed., pp. 215-223, Springer Verlag, 1995.[31] L.R.B. Schomaker, Using Stroke- or Character-Based Self-Organizing Maps in the Recognition of On-Line, Connected Cursive Script Pattern Recognition, vol. 26, no. 3, pp. 443-450, 1993.[32] E.G. Schukat-Talamazzini, Automatische Spracherkennung Grundlagen, Statistische Modelle und Effiziente Algorithmen. Friedr. Vieweg&Sohn, 1995.[33] G. Seni, R.K. Srihari, and N. Nasrabadi, “Large Vocabulary Recognition of On-Line Handwritten Cursive Words,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 7, pp. 757-762, July 1996.[34] K. Simon, Erkennung von Handgeschriebenen Wörtern mit CSDTW Master's thesis, Albert-Ludwigs-Universität Freiburg, Institut für Informatik,http:/lmb.informatik.uni-freiburg.de/, 2003.[35] P. Smyth, Clustering Sequences with Hidden Markov Models Advances in Neural Information Processing Systems 9, pp. 648-654, 1997.[36] S. Theodoridis and K. Koutroumbas, Pattern Recognition. Academic Press, 1999.[37] V. Vuori, J. Laaksonen, E. Oja, and J. Kangas, Experiments with Adaptation Strategies for a Prototype-Based Recognition System for Isolated Handwritten Characters Int'l J. Document Analysis and Recognition, vol. 3, no. 2, pp. 150-159, 2001.[38] L.G. Vuurpijl and L.R.B. Schomaker, Finding Structure in Diversity: A Hierarchical Clustering Method for the Categorization of Allographs in Handwriting Proc. Fourth Int'l Conf. Document Analysis and Recognition, pp. 387-393, 1997.[39] G.W. Wilfong, F. Sinden, and L. Ruedisueli, On-Line Recognition of Handwritten Symbols IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, pp. 935-940, 1996.[40] R. Zhang, A.I. Rudnicky, A Large Scale Clustering Scheme for Kernel k-Means Proc. 16th Int'l Conf. Pattern Recognition, 2002.