TITLE

Implementation of Real-Time Static Hand Gesture Recognition Using Artificial Neural Network

TYPE

Journal Article

Abstract

This paper implements static hand gesture recognition in recognizing the alphabetical sign from A to Z, number from 0 to 9, and additional punctuation mark such as Period, Question Mark, and Space in Sistem Isyarat Bahasa Indonesia (SIBI). Hand gestures are obtained by evaluating the contour representation from image segmentation of the glove wore by user. Then, it is classified using Artificial Neural Network (ANN) based on the training model previously built from 100 images for each gesture. The accuracy rate of hand gesture translation is calculated to be 90%. Moreover, speech translation recognizes NATO phonetic letter as the speech input for translation.

Citation

Yusnita L, Rosalina, Roestam R and Wahyu R. B. (2017). Implementation of Real-Time Static Hand Gesture Recognition Using Artificial Neural Network. CommIT, 11 (2), 85-91.

Keywords

Static Hand Gesture, Artificial Neural Network, Speech Translation, SIBI

Published On

CommIT

Author

Lita Yusnita

  • Rosalina
  • Rusdianto Roestam
  • Raden Bagio Wahyu

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