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Development of a hierarchical dynamic keyboard character recognition system using trajectory features and scale-invariant holistic modeling of characters

  • Songhita MisraEmail author
  • R. H. Laskar
Original Research
  • 27 Downloads

Abstract

A robust, intuitive, effortless, and novel dynamic hand gesture based virtual keyboard system is developed in this study. Firstly, a new hierarchical approach is applied, which, based on self-coarticulation and position features, effectively sub-groups a large gesture vocabulary. Additionally, new trajectory features are proposed which shall extract the local structural statistics of the gestures. All state-of-art models are based on temporal trajectory features which are based on the frame-wise 2D sequential path it followed. Due to this, the trajectory features are path dependent and vulnerable to trajectory noises or any other variations in pattern, speed, or scale. In contrast to this, an image-based approach of gesture recognition has been proposed in this study, which is independent of the sequential gesturing path of the gesture. Since the image-models (a holistic view) are not obtained frame-wise, unlike existing image-models, they are pattern, speed, and scale invariant in nature and also immune to trajectory distortions. To this end, image-based features and significant trajectory features are fused to develop a hybrid hierarchical classification model which exhibits an exceptional increase in accuracy by 3.9% as compared to baseline non-hierarchical trajectory based model using an Artificial neural network (ANN). Classification models such as Voronoi diagram based classifier (VDBC) and neuro-fuzzy (NF) classifier have also been explored and displayed motivating performance. Reduction in misclassification has been observed for gestures such as ‘(and)’, ‘{and}’, ‘0 and O’, ‘Z and 2’. The present system can also identify any static/dynamic imposters present in the gesture environment.

Keywords

Holistic model Dynamic gestures Keyboard characters Hierarchical model Trajectory features 

Notes

Acknowledgements

Authors are thankful to the Ministry of Electronics and Information Technology, Government of India for the financial support under the Visvesvaraya Ph.D. scheme. Authors also acknowledge Speech and Image Processing Laboratory, ECE Dept., National Institute of Technology Silchar, India, for providing all necessary facilities to carry out the research work.

Funding

The study was funded by Ministry of Electronics and Information Technology, Government of India with Grant no. PhD-MLA/4(74)/2015-16.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ECENIT SilcharSilcharIndia

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