Dynamic hand gesture to text using leap motion

Nur Aliah Nadzirah Jamaludin, Ong Huey Fang

    Research output: Contribution to journalArticleResearchpeer-review


    This paper presents a prototype for converting dynamic hand gestures to text by using a device called Leap Motion. It is one of the motion tracking technologies, which could be used for recognising hand gestures without the need of wearing any external devices or capturing any images and videos. In this study, five custom dynamic hand gestures of American Sign Language were created with Leap Motion to measure the recognition accuracy of the proposed prototype using the Geometric Template Matching, Artificial Neural Network, and Cross-Correlation algorithms. The experimental results showed that the prototype achieved recognition accuracy of more than 90% in the training phase and about 60% in the testing phase.

    Original languageEnglish
    Pages (from-to)199-204
    Number of pages6
    JournalInternational Journal of Advanced Computer Science and Applications
    Issue number11
    Publication statusPublished - 2019


    • American sign language
    • Artificial neural network
    • Cross-correlation
    • Dynamic hand gesture
    • Geometric template matching
    • Leap motion

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