Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network
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 potentiall...
| Główni autorzy: | , , , |
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| Format: | Conference item |
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Institute of Electrical and Electronics Engineers
2017
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| _version_ | 1826300346657406976 |
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| author | Yang, W Jin, L Ni, H Lyons, T |
| author_facet | Yang, W Jin, L Ni, H Lyons, T |
| author_sort | Yang, W |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T05:15:45Z |
| format | Conference item |
| id | oxford-uuid:dd1ec888-c558-4385-8f48-4efcb867b682 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T05:15:45Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:dd1ec888-c558-4385-8f48-4efcb867b6822022-03-27T09:22:45ZRotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural networkConference itemhttp://purl.org/coar/resource_type/c_5794uuid:dd1ec888-c558-4385-8f48-4efcb867b682Symplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2017Yang, WJin, LNi, HLyons, TThe 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. |
| spellingShingle | Yang, W Jin, L Ni, H Lyons, T Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network |
| title | Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network |
| title_full | Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network |
| title_fullStr | Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network |
| title_full_unstemmed | Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network |
| title_short | Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network |
| title_sort | rotation free online handwritten character recognition using dyadic path signature features hanging normalization and deep neural network |
| work_keys_str_mv | AT yangw rotationfreeonlinehandwrittencharacterrecognitionusingdyadicpathsignaturefeatureshangingnormalizationanddeepneuralnetwork AT jinl rotationfreeonlinehandwrittencharacterrecognitionusingdyadicpathsignaturefeatureshangingnormalizationanddeepneuralnetwork AT nih rotationfreeonlinehandwrittencharacterrecognitionusingdyadicpathsignaturefeatureshangingnormalizationanddeepneuralnetwork AT lyonst rotationfreeonlinehandwrittencharacterrecognitionusingdyadicpathsignaturefeatureshangingnormalizationanddeepneuralnetwork |