Online gesture-based interaction with visual oriental characters based on manifold learning

Yi Wang, Juncheng Liu, Xin Fan, Xiangjian He, Qi Jia, Renjie Gao

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)


Online gesture-based interaction with characters has become a more natural and informative human-computer interface with the popularity of new interactive devices (e.g., Kinect and Leap Motion). In this paper, a new feature descriptor named Segmented Directed-edge Vector (SDV) is proposed. This simple and yet quite effective descriptor is able to capture the characteristics of visual oriental characters. Moreover, we explicitly build the mappings from SDVs to features in a subspace by a modified Locality Preserving Projections (LPP) method with stroke class constraints. These mappings can yield meaningful subspace structures for larger character sets. Extensive experiments on the online interactive system demonstrate the robustness of our method to various issues in gesture-based characters input, such as unnatural breaks, overlapped or distorted radicals, and unconscious or quivering trajectories. Our system can still achieve accurate recognition when accumulative errors occur with complex characters.

Original languageEnglish
Pages (from-to)123-131
Number of pages9
JournalSignal Processing
Publication statusPublished - May 2015
Externally publishedYes


  • Feature descriptors
  • Graph-optimized locality preserving projections
  • Human-computer interface
  • Visual oriental characters

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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