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A Practical Approach for Writer-Dependent Symbol Recognition Using a Writer-Independent Symbol Recognizer
November 2007 (vol. 29 no. 11)
pp. 1917-1926
We present a practical technique for using a writerindependent recognition engine to improve the accuracy and speed while reducing the training requirements of a writerdependent symbol recognizer. Our writer-dependent recognizer uses a set of binary classifiers based on the AdaBoost learning algorithm, one for each possible pairwise symbol comparison. Each classifier consists of a set of weak learners, one of which is based on a writer-independent handwriting recognizer. During online recognition, we also use the n-best list of the writerindependent recognizer to prune the set of possible symbols and thus reduce the number of required binary classifications. In this paper, we describe the geometric and statistical features used in our recognizer and our all-pairs classification algorithm. We also present the results of experiments that quantify the effect incorporating a writer-independent recognition engine into a writer-dependent recognizer has on accuracy, speed, and user training time.

[1] E. Allwein, R. Schapire, and Y. Singer, “Reducing Multiclass to Binary: A Unifying Approach to Margin Classifiers,” J. Machine Learning Research, vol. 1, pp. 113-141, 2000.
[2] 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.
[3] A. Belaid and J. Haton, “A Syntactic Approach for Handwritten Formula Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 1, pp. 105-111, Jan. 1984.
[4] A. Biem, “Minimum Classification Error Training for Online Handwriting Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 7, pp. 1041-1051, July 2006.
[5] A. Brakensiek, A. Kosmala, and G. Rigoll, “Comparing Adaptation Techniques for Online Handwriting Recognition,” Proc. Int'l Conf. Document Analysis and Recognition, pp. 486-490, 2001.
[6] K. Chan and D. Yeung, “Mathematical Expression Recognition: A Survey,” Int'l J. Document Analysis and Recognition, vol. 3, no. 1, pp.3-15, Jan. 2000.
[7] S.D. Connell and A.K. Jain, “Writer Adaptation for Online Handwriting Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 329-346, Mar. 2002.
[8] S.D. Connell and A.K. Jain, “Template-Based On-Line Character Recognition,” Pattern Recognition, vol. 34, no. 1, pp. 1-14, Jan. 2000.
[9] A.M. Day, J.R. Parks, and P.J. Pobgee, “On-Line Written Input to Computers,” Machine Perception of Pictures and Patterns, pp. 233-240, 1972.
[10] V. Deepu, S. Madhvanath, and A.G. Ramakrishnan, “Principal Component Analysis for Online Handwritten Character Recognition,” Proc. 17th Int'l Conf. Pattern Recognition, pp. 327-330, 2004.
[11] Y. Dimitriadis and J. Coronado, “Towards an Art-Based Mathematical Editor that Uses On-Line Handwritten Symbol Recognition,” Pattern Recognition, vol. 28, no. 6, pp. 807-822, June 1995.
[12] A. Donahey, “Character Recognition System and Method,” USpatent 3,996,557, 1976.
[13] Y. Freund and R. Schapire, “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” J. Computer and System Sciences, vol. 55, no. 1, pp. 119-139, Aug. 1997.
[14] J. Friedman, “Another Approach to Polychotomous Classification,” technical report, Stanford Univ., 1996.
[15] G.F. Groner, “Real-Time Recognition of Hand-Printed Symbols,” Pattern Recognition, L.N. Kanal ed., pp. 103-108, 1968.
[16] Guyon, I.L. Schomaker, R. Plamondon, M. Liberman, and S. Janet, “UNIPEN Project of On-Line Data Exchange and Recognizer Benchmarks,” Proc. 12th Int'l Conf. Pattern Recognition, pp. 29-33, Oct. 1994.
[17] T. Hastie and R. Tibshirani, “Classification by Pairwise Coupling,” The Annals of Statistics, vol. 26, no. 2, pp. 451-471, Apr. 1998.
[18] R. Jarrett and P. Su, Building Tablet PC Applications. Microsoft Press, 2003.
[19] D. Kerrick and A. Bovik, “Microprocessor-Based Recognition of Hand-Printed Characters from a Tablet Input,” Pattern Recognition, vol. 21, no. 5, pp. 525-537, May 1988.
[20] M. Koschinski, H.-J. Winkler, and M. Lang, “Segmentation and Recognition of Symbols within Handwritten Mathematical Expressions,” Proc. Int'l Conf. Acoustics, Speech, Signal Processing, pp.2439-2442, 1995.
[21] A. Kosmala and G. Rigoll, “On-Line Handwritten Formula Recognition Using Statistical Methods,” Proc. 14th Int'l Conf. Pattern Recognition, pp. 1306-1308, 1998.
[22] J. LaViola, “Mathematical Sketching: A New Approach to Creating and Exploring Dynamic Illustrations,” PhD dissertation, Dept. of Computer Science, Brown Univ., May 2005.
[23] X. Li and D. Yeung, “On-Line Handwritten Alphanumeric Character Recognition Using Dominant Points in Strokes,” Pattern Recognition, vol. 30, no. 1, pp. 31-44, Jan. 1997.
[24] R. Marzinkewitsch, “Operating Computer Algebra Systems by Hand-Printed Input,” Proc. Int'l Symp. Symbolic and Algebraic Computation, pp. 411-413, 1991.
[25] C. Mathis and T.M. Breuel, “Classification Using a Hierarchical Bayesian Approach,” Proc. 16th Int'l Conf. Pattern Recognition, pp.IV: 103-106, 2002.
[26] N.E. Matsakis, “Recognition of Handwritten Mathematical Expressions,” master's thesis, Dept. of Electrical Eng. and Computer Science, Massachusetts Inst. of Tech nology, 1999.
[27] E. Miller and P. Viola, “Ambiguity and Constraint in Mathematical Expression Recognition,” Proc. 15th Nat'l Conf. Artificial Intelligence, pp. 784-791, 1998.
[28] Y. Nakayama, “A Prototype Pen-Input Mathematical Formula Editor,” Proc. World Conf. Educational Multimedia and Hypermedia, pp. 400-407, 1993.
[29] 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.
[30] V.M. Powers, “Pen Direction Sequences in Character Recognition,” Pattern Recognition, vol. 5, pp. 291-302, Mar. 1973.
[31] L. Prevost and M. Milgram, “Automatic Allograph Selection and Multiple Expert Classification for Totally Unconstrained Handwritten Character Recognition,” Proc. 14th Int'l Conf. Pattern Recognition (ICPR '98), pp. 381-383, 1998.
[32] D. Rubine, “Specifying Gestures by Example,” Proc. ACM 18th Ann. Conf. Computer Graphics and Interactive Techniques, pp. 329-337, 1991.
[33] P. Sarkar and G. Nagy, “Style Consistent Classification of Isogenous Patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 88-98, Jan. 2005.
[34] R. Schapire, “A Brief Introduction to Boosting,” Proc. 16th Int'l Joint Conf. Artificial Intelligence, pp. 1401-1406, 1999.
[35] H. Schwenk and Y. Bengio, “AdaBoosting Neural Networks: Application to On-Line Character Recognition,” Lecture Notes in Computer Science, vol. 1327, pp. 967-972, 1997.
[36] M. Shilman, P. Viola, and K. Chellapilla, “Recognition and Grouping of Handwritten Text in Diagrams and Equations,” Proc. Ninth Int'l Workshop Frontiers in Handwriting Recognition, pp. 569-574, 2002.
[37] S. Smithies, K. Novins, and J. Arvo, “A Handwriting-Based Equation Editor,” Proc. Graphics Interface Conf., pp. 84-91, 1999.
[38] J. Subrahmonia, K. Nathan, and M. Perrone, “Writer Dependent Recognition of Online Unconstrained Handwriting,” Proc. Int'l Conf. Acoustics, Speech, and Signal Processing, vol. 6, pp. 3478-3481, 1996.
[39] C. Tappert, C.Y. Seun, and T. Wakahara, “The State of the Art in On-Line Handwriting Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 8, pp. 787-808, Aug. 1990.
[40] E. Weisstein, CRC Concise Encyclopedia of Mathematics. Chapman and Hall/CRC, 1998.
[41] H.-J. Winkler, “Symbol Recognition in Handwritten Mathematical Formulas,” Proc. Int'l Workshop Modern Modes of Man-Machine Comm., pp. 7/1-7/10, June 1994.

Index Terms:
Handwriting recognition, AdaBoost, writer dependence, writer independence, pairwise classification, real-time systems
Citation:
Joseph J. LaViola Jr., Robert C. Zeleznik, "A Practical Approach for Writer-Dependent Symbol Recognition Using a Writer-Independent Symbol Recognizer," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 11, pp. 1917-1926, Nov. 2007, doi:10.1109/TPAMI.2007.1109
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