This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image
November 2006 (vol. 28 no. 11)
pp. 1724-1737
Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the Edge Continuity Relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for the node of degree 4 and a theoretical approach is presented for the node of degree higher than 4 by introducing certain reasonable assumptions. In the second phase, we identify double-traced lines by employing maximum weighted matching. This makes it possible to transform the problem of obtaining possible ECRs at odd-degree node to that at even-degree node. In the final, global phase, we find all the candidates of single-stroked paths by depth first search and select the best one by evaluating SLALOM smoothness. Experiments on static images converted from online data in the Unipen database show that our method achieves a restoration rate of 96.0 percent.

[1] 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.
[2] T. Huang and M. Yasuhara, “A Total Stroke SLALOM Method for Searching for the Optimal Drawing Order of Off-Line Handwriting,” Proc. IEEE Systems, Man, and Cybernetics Soc., pp. 2789-2794, 1995.
[3] H. Bunke, R. Ammann, G. Kaufmann, T.M. Ha, M. Schenkel, R. Seiler, and F. Eggimann, “Recovery of Temporal Information of Cursively Handwritten Words for On-Line Recognition,” Proc. Fourth Int'l Conf. Document Analysis and Recognition, pp. 931-935, 1997.
[4] Y. Kato and M. Yasuhara, “Recovery of Drawing Order from Single-Stroke Handwriting Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 9, pp. 938-949, Sept. 2000.
[5] M.K. Babcock and J.J. Freyd, “Perception of Dynamic Information in Static Handwritten Forms,” Am. J. Psychology, vol. 101, no. 1, pp.111-130, 1988.
[6] R. Plamondon and C.M. Privitera, “The Segmentation of Cursive Handwriting: An Approach Based on Off-Line Recovery of the Motor-Temporal Information,” IEEE Trans. Image Processing, vol. 8, no. 1, pp. 80-91, 1991.
[7] S. Kadota, S. Hayashi, M. Yakamoto, S. Yajima, and M. Yasuda, “Recognition of Handprinted Characters by Nonlinear Elastic Matching,” Proc. Int'l Conf. Pattern Recognition, pp. 113-1128, 1976.
[8] S. Lee and J.C. Pan, “Offline Tracing and Representation of Signatures,” IEEE Trans. Systems, Man, and Cybernetics, vol. 22, no. 4, pp. 755-771, 1992.
[9] D.S. Doermann and A. Rosenfeld, “Recovery of Temporal Information from Static Images of Handwriting,” Int'l J. Computer Vision, vol. 15, no. 1-2, pp. 143-164, 1995.
[10] D.S. Doermann and A. Rosenfeld, “Recovery of Temporal Information from Static Images of Handwriting,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 162-168, 1992.
[11] G. Boccignone, A. Chianese, L.P. Cordella, and A. Marcelli, “Recovering Dynamic Information from Static Handwriting,” Pattern Recognition, vol. 26, no. 3, pp. 409-418, 1993.
[12] V. Govindaraju and S. Srihari, “Separating Handwritten Text from Non-Textual Interference,” From Pixels to Features: II, J. Simon and S. Impedovo eds., pp. 17-28, North-Holland: Elsevier Science, 1992.
[13] K. Liu, Y.S. Huang, and C.Y. Suen, “Robust Stroke Segmentation Method for Handwritten Chinese Character Recognition,” Proc. Int'l Conf. Document Analysis and Recognition, pp. 211-215, 1997.
[14] K. Liu, Y.S. Huang, and C.Y. Suen, “Identification of Fork Points on the Skeletons of Handwritten Chinese Characters,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 10, pp. 1095-1100, Oct. 1999.
[15] S. Jäger, “Recovering Writing Traces in Off-Line Handwriting Recognition: Using a Global Optimization Technique,” Proc. Int'l Conf. Pattern Recognition, pp. 150-154, 1996.
[16] S. Jaeger, “Recovering Dynamic Information from Static, Handwritten Word Images,” PhD thesis, 1998.
[17] P.M. Lallican, C. Viard-gaudin, and S. Knerr, “From Off-Line to On-Line Handwriting Recognition,” Proc. Int'l Workshop Frontiers in Handwriting Recognition, pp. 303-312, Sept. 2000.
[18] Y. Kato and M. Yasuhara, “Recovery of Drawing Order from Scanned Images of Multi-Stroke Handwriting,” Proc. Fifth Int'l Conf. Document Analysis and Recognition, pp. 261-264, 1999.
[19] Y. Qiao and M. Yasuhara, “Recovering Dynamic Information from Static Handwritten Images,” Proc. Int'l Workshop Frontiers in Handwriting Recognition, pp. 118-123, Oct. 2004.
[20] Y. Isomichi, “Inverse-Quantization Method for Digital Signals and Images,” IEICE Trans., vol. J64-A, no. 4, pp. 285-292, 1981 (in Japanese).
[21] Y. Qiao, M. Nishiara, and M. Yasuhara, “A Novel Approach to Recover Writing Order from Single Stroke Offline Handwritten Images,” Proc. Eighth Int'l Conf. Document Analysis and Recognition, pp. 227-231, 2005.
[22] H.D. Chang and J.F. Wang, “Preclassification for Handwritten Chinese Character Recognition by a Peripheral Shape Coding Method,” Pattern Recognition, vol. 26, no. 5, pp. 711-719, 1993.
[23] L. Lam, S.-W. Lee, and C.Y. Suen, “Thinning Methodologies—A Comprehensive Survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 9, pp. 869-885, Sept. 1992.
[24] T. Mitchell, Machine Learning. Boston: McGraw-Hill, 1997.
[25] I.T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
[26] I. Guyon, L. Schomaker, R. Plamondon, M. Liberman, and S. Janet, “UNIPEN Project of On-Line Data Exchange and Recognizer Benchmarks,” Proc. Int'l Conf. Pattern Recognition, pp. 29-33, Oct. 1994.
[27] J. Edmonds, “Maximum Matching and a Polyhedron with (0, 1) Vertices,” J. Research Nat'l Bureau of Standards, vol. 69(b), pp.125-130, 1965.
[28] H. N. Gabow, “Data Structures for Weighted Matching and Nearest Common Ancestors with Linking,” Proc. Ann. ACM-SIAM Symp. Discrete Algorithms (SODA), pp. 434-443, 1990.
[29] MATHPROG, Solver for the Maximum Weight Matching Problem, http://elib.zib.de/pub/Packages/mathprog/ matchingweighted/. 2006.
[30] J. Edmonds and E.L. Johnson, “Matching, Euler Tours and the Chinese Postman,” Math. Programming, vol. 5, pp. 88-124, 1973.
[31] R.E. Tarjan, “Depth First Search and Linear Graph Algorithms,” SIAM J. Computing, vol. 1, no. 1, pp 146-160, 1972.
[32] L. Rabiner and B.H. Juang, Fundamentals of Speech Recognition, chapter 4. Prentice-Hall, 1993.
[33] Y. Qiao and M. Yasuhara, “Reccovering Drawing Order from Offline Handwritten Image Using Direction Context and Optimal Euler Path,” Proc. 31st Int'l Conf. Acoustics, Speech, and Signal Processing, 2006.
[34] R.O. Duda, P.E. Hart, and G. David, Pattern Classification, second ed., chapter 4.5. New York: Wiley, 2000.
[35] T. Steinherz, E. Rivlin, N. Intrator, and P. Neskovic, “An Integration of Online and Pseudo-Online Information for Cursive Word Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 669-683, May, 2005.

Index Terms:
Handwriting recognition, writing order restoration, edge continuity relation, temporal information, graph matching, Euler path.
Citation:
Yu Qiao, Mikihiko Nishiara, Makoto Yasuhara, "A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp. 1724-1737, Nov. 2006, doi:10.1109/TPAMI.2006.216
Usage of this product signifies your acceptance of the Terms of Use.