A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image

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]  Robert E. Tarjan,et al.  Depth-First Search and Linear Graph Algorithms , 1972, SIAM J. Comput..

[2]  Yu Qiao,et al.  Recovering Drawing Order from Offline Handwritten Image Using Direction Context and Optimal Euler Path , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[3]  Ching Y. Suen,et al.  Identification of Fork Points on the Skeletons of Handwritten Chinese Characters , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Stefan Jäger,et al.  Recovering writing traces in off-line handwriting recognition: using a global optimization technique , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[5]  Azriel Rosenfeld,et al.  Recovery of temporal information from static images of handwriting , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Yu Qiao,et al.  A novel approach to recover writing order from single stroke offline handwritten images , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[7]  Horst Bunke,et al.  Recovery of temporal information of cursively handwritten words for on-line recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[8]  Harold N. Gabow,et al.  Data structures for weighted matching and nearest common ancestors with linking , 1990, SODA '90.

[9]  Makoto Yasuhara,et al.  Recovery of drawing order from scanned images of multi-stroke handwriting , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[10]  Jack Edmonds,et al.  Maximum matching and a polyhedron with 0,1-vertices , 1965 .

[11]  M. Babcock,et al.  Perception of dynamic information in static handwritten forms. , 1988, The American journal of psychology.

[12]  Ching Y. Suen,et al.  Thinning Methodologies - A Comprehensive Survey , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Yoshinori Isomichi.  Inverse quantization method for digital signals and images—area approximation type , 1981 .

[14]  Stefan Jäger.  Recovering dynamic information from static, handwritten word images: bridging the gap between on-line and off-line handwriting recognition , 1998 .

[15]  Yu Qiao,et al.  Recovering dynamic information from static handwritten images , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[16]  LiuKe,et al.  Identification of Fork Points on the Skeletons of Handwritten Chinese Characters , 1999 .

[17]  Sukhan Lee,et al.  Offline tracing and representation of signatures , 1992, IEEE Trans. Syst. Man Cybern..

[18]  Jack Edmonds,et al.  Matching, Euler tours and the Chinese postman , 1973, Math. Program..

[19]  Ching Y. Suen,et al.  Robust stroke segmentation method for handwritten Chinese character recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[20]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[21]  Makoto Yasuhara,et al.  Recovery of Drawing Order from Single-Stroke Handwriting Images , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Jhing-Fa Wang,et al.  Preclassification for handwritten chinese character recognition by a peripheral shape coding method , 1993, Pattern Recognit..

[23]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Ehud Rivlin,et al.  An integration of online and pseudo-online information for cursive word recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Isabelle Guyon,et al.  UNIPEN project of on-line data exchange and recognizer benchmarks , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[26]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[27]  Angelo Chianese,et al.  Recovering dynamic information from static handwriting , 1993, Pattern Recognit..

[28]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[29]  Tong Huang,et al.  A total stroke SLALOM method for searching for the optimal drawing order of off-line handwriting , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[30]  Christian Viard-Gaudin,et al.  From Off-line to On-line Handwriting Recognition , 2004 .