Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network

Author: 

Yang, W
Jin, L
Ni, H
Lyons, T

Publication Date: 

1 January 2016

Journal: 

Proceedings - International Conference on Pattern Recognition

Last Updated: 

2019-08-15T21:28:47.313+01:00

DOI: 

10.1109/ICPR.2016.7900273

page: 

4083-4088

abstract: 

© 2016 IEEE. The path signature feature (PSF) which was initially introduced in rough paths theory as a branch of stochastic analysis, has recently been successfully applied to the field of pattern recognition for extracting sufficient quantity of information contained in a finite trajectory, but with potentially high dimension. In this paper, we propose a variation of path signature representation, namely the dyadic path signature feature (D-PSF), to fully characterize the trajectory using a hierarchical structure to solve the rotation-free online handwritten character recognition (OLHCR) problem. We adopt the deep neural network (DNN) as classifier, and investigate three hanging normalization methods to improve the robustness of the DNN to rotational distortions. Extensive experiments on digits, English letters, and Chinese radicals demonstrated that the proposed D-PSF, jointly with hanging normalization and DNN, achieved very promising results for rotated OLHCR, significantly outperforming previous methods.

Symplectic id: 

698400

Submitted to ORA: 

Submitted

Publication Type: 

Conference Paper

ISBN-13: 

9781509048472