Sign language recognition by means of common spatial patterns: An analysis

Abstract

Currently there are around 466 million hard of hearing people and this amount is expected to grow in the coming years. Despite the efforts that have been made, there is a communication barrier between deaf and hard of hearing signers and non-signers in environments without an interpreter. Different approaches have been developed lately to try to deal with this issue. In this work, we present an Argentinian Sign Language (LSA) recognition system which uses hand landmarks extracted from videos of the LSA64 dataset in order to distinguish between different signs. Different features are extracted from the signals created with the hand landmarks values, which are first transformed by the Common Spatial Patterns (CSP) algorithm. CSP is a dimensionality reduction algorithm and it has been widely used for EEG systems. The features extracted from the transformed signals have been then used to feed different classifiers, such as Random Forest (RF), K-Nearest Neighbors (KNN) or Multilayer Perceptron (MLP). Several experiments have been performed from which promising results have been obtained, achieving accuracy values between 0.90 and 0.95 on a set of 42 signs.

A Four-Stage Mahalanobis-Distance-Based Method for Hand Posture Recognition

“…Once the skeleton data is determined, various classification methods are used. There are known solutions using k-nearest neighbors classifier (KNN) [9], support vector machine (SVM) [10][11][12], rule-based classifier [13], artificial neural networks (ANN) [14][15][16][17], random forest (RF) [12,18], gradient boosting (GB) [11,12], and various models based on deep learning [15,[18][19][20][21][22][23][24][25][26][27][28][29][30].…”

A machine learning-driven web application for sign language learning

“…A significant portion of the global population faces hearing impairment, which can greatly hinder their ability to engage in meaningful verbal conversations. According to the World Health Organization (WHO), approximately 6% of the world’s population experiences some form of hearing impairment, with nearly 5% requiring therapeutic interventions for “disabling” hearing loss ( World Health Organization, 2021 ; Madhiarasan, 2022 ; Rodríguez-Moreno et al, 2022 ). Alarmingly, projections indicate that by the year 2050, as many as 900 million individuals, or one in every ten people, could be affected by debilitating hearing loss ( Elsayed and Fathy, 2020 ; Altememe, 2021 ).…”