Representing and Characterizing Handwritten Mathematical Symbols through Succinct Functional Approximation

We model on-line ink traces for a set of 219 symbols to "best fit" low-degree polynomial series. Using a collection of mathematical writing samples, we find that in many cases this provides a succinct way to model the stylus movements of actual test users. Furthermore, even without further similarity-processing, the polynomial coefficients from the writing samples form clusters which often contain the same character as written by different users. We find this style of characterization to be an attractive tool due to the suitability of the representation to computation and mathematical analysis.

[1]  Lei Huang,et al.  Multiresolution recognition of offline handwritten Chinese characters with wavelet transform , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[2]  C. W. Clenshaw Chebyshev series for mathematical functions , 1962 .

[3]  Yuan Yan Tang,et al.  A novel approach to optical character recognition based on ring-projection-wavelet-fractal signatures , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[4]  S. Sathiya Keerthi,et al.  A study of representations for pen based handwriting recognition of Tamil characters , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[5]  Hans-Jürgen Winkler,et al.  HMM-based handwritten symbol recognition using on-line and off-line features , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[6]  Ching Y. Suen,et al.  Hybrid feature extraction and feature selection for improving recognition accuracy of handwritten numerals , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[7]  Andrew F. Laine,et al.  Wavelet descriptors for multiresolution recognition of handprinted characters , 1995, Pattern Recognit..

[8]  David M. Mount,et al.  A local search approximation algorithm for k-means clustering , 2002, SCG '02.

[9]  Dit-Yan Yeung,et al.  Elastic structural matching for online handwritten alphanumeric character recognition , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[10]  Ching Y. Suen,et al.  The State of the Art in Online Handwriting Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Véronique Eglin,et al.  Evaluation of Handwriting Similarities Using Hermite Transform , 2006 .