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

Authors

  • Lita Yusnita President University
  • Rosalina Rosalina President University
  • Rusdianto Roestam
  • R. B. Wahyu President University

DOI:

https://doi.org/10.21512/commit.v11i2.2282

Keywords:

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

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.

Dimensions

Plum Analytics

Author Biography

Rusdianto Roestam

President University

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Published

2017-10-31
Abstract 775  .
PDF downloaded 651  .