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

• Published in: 2016 23rd International Conference on Pattern Recognition (ICPR) pp. 4083 - 4088
• Main Authors: Weixin Yang, Lianwen Jin, Hao Ni, Lyons, Terry
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2016
• Subjects: Biological neural networks; Character recognition; Distortion; Feature extraction; Handwriting recognition; neural network; online handwritten character recognition; path signature feature; rotation normalization; rotation-free recognition; Trajectory
• DOI: 10.1109/ICPR.2016.7900273

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.