Comparative analysis of support vector machine and k-nearest neighbors with a pyramidal histogram of the gradient for sign language detection

  • Imantoko Imantoko Universitas Teknologi Yogyakarta
  • Arief Hermawan Universitas Teknologi Yogyakarta
  • Donny Avianto Universitas Teknologi Yogyakarta

Abstract

The communication method using sign language is very efficient considering that the speed of information delivery is closer to verbal communication (speaking) compared to writing or typing. Because of this, sign language is often used by people who are deaf, speech impaired, and normal people to communicate. To make sign language translation easier, a system is needed to translate symbols formed from hand movements (in the form of images) into text or sound. This study aims to compare performance such as accuracy and
computation time of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) with Pyramidal Histogram of Gradient (PHOG) for feature extraction, to know which one is better at recognizing sign language. Yield, both combined methods PHOG-SVM and PHOG-KNN can recognize images from hand movements that form certain symbols. The system built using the SVM classification produces the highest accuracy of 82% at PHOG level 3, while the system built with the KNN classification produces the highest accuracy of 78% at PHOG level 2. The total computation time of the fastest training and testing by the SVM model is 236.53 seconds at PHOG level 3, while the KNN model is 78.27 seconds at PHOG level 3. In terms of accuracy, PHOG-SVM is better, but in terms of computation time, PHOG-KNN takes the place.

Downloads

Download data is not yet available.
Published
Jul 15, 2021
How to Cite
IMANTOKO, Imantoko; HERMAWAN, Arief; AVIANTO, Donny. Comparative analysis of support vector machine and k-nearest neighbors with a pyramidal histogram of the gradient for sign language detection. Matrix : Jurnal Manajemen Teknologi dan Informatika, [S.l.], v. 11, n. 2, p. 107-118, july 2021. ISSN 2580-5630. Available at: <http://ojs.pnb.ac.id/index.php/matrix/article/view/2433>. Date accessed: 29 sep. 2021. doi: http://dx.doi.org/10.31940/matrix.v11i2.2433.