Title

Hand Gesture Recognition Using Computer Vision Applied to Colombian Sign Language

DOI

https://doi.org/10.1007/978-3-030-45096-0_26

Document Type

Conference Proceeding

Publication Date

1-1-2020

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

In this document we describe a hand gesture classification system of the Colombian Sign Language for both dynamic and static signs, based on Computer Vision and Machine learning. The proposed processes sequence is divided in four stages: acquisition of RGB-D image, extraction of the blob closest to the sensor, detection and validation of the hand, and classification of the sign entered. The results obtained are for multi-class classifiers with a self-captured dataset of 3.600 samples. As a conclusion we found that the best choice for descriptor-classifier according to sign type are HOG-SVM for static signs with an accuracy of 98%, and SVM classifier besides the trajectory-based descriptor with an accuracy of 94%.

Volume

12014 LNCS

First Page

207

Last Page

214

ISSN

03029743

ISBN

9783030450953

Identifier

SCOPUS_ID:85083997406

Documento original

Compartir

COinS